Financial Services - Incedo Inc.

What is Contract Pull Through?

The pharma sales team engages in contracts with brands, hospitals, clinics, infusion centers, doctor offices, IDNs, ONA, GPOs, and other networks. These networks are often referred to as pharma accounts, and contracts are lined up to improve overall sales, market share, and profitability. Contracts with these accounts are based on various factors such as rebate percentage, formulary tiers, and performance-based fees.

Pharma’s gross contracted sales is a large multi billion dollar opportunity which is growing at a rapid pace. This is a big opportunity for the commercial team to boost sales with these accounts. Pharma companies analyze data on contracts, rebates, terms, and tiers to see how accounts perform. This enables them to identify accounts that are doing poorly and the ones that are doing exceptionally well.

We may define Contact Pull Through, as the analysis of –

How much an account has purchased (sales) by contract program and by brand;
How much they’ve received in discounts (rebates, chargebacks);
How they are doing against their baselines; and
Where are the opportunities to buy more and save more?

Why does pharma need to focus on Contract Pull Through?

Large and mid-size pharma companies have Contract Pull Through from the accounts, as the top-of-mind problem, as even increasing effectiveness by 2-5% would mean savings to the tune of millions of dollars.

Based on our experiences working with the client’s market access team – we realized that the organization delegated the task of supporting field pull-through entirely to its payer account managers. These executives reported spending 75% of their time creating and pulling reports, time that could have been better spent with customers or in more strategic dialogue with the field members.

The key stakeholders for Contract Pull Through are the field team members i.e., Business Engagement Managers (BEMs) and Healthcare Market Directors (HDs), who need to focus on Contract Pull Through for –

Generating contract awareness and pull through for major providers in their ecosystems
Creating awareness around the contracts/terms offered by pharma firms for the products
Engaging with customers to show their historical performance and current performance

Some specific Contract Pull Through use cases the pharma account team focuses on include:

A portfolio purchasing summary of the account, which enables the account to understand how much volume the account has bought, and the savings received from pharma products
Product contribution at an account level, which enables the understanding of how much volume is coming from each pharma product
Contract eligibility of an account, to understand which contracts are available at a certain account

Other business insights that Contract Pull Through data may help pharma companies are around –

How is the account performing as compared to others, in their ecosystem/region?
Which is the account’s dominant payer, and how does that payer work at a national level?
How much can this account purchase to reach the next tier?

Use case deep dive: Portfolio purchasing summary of an account

A portfolio purchasing summary of the account is one of the critical use cases handled through Contract Pull data – what the Contract Pull Through team is looking for is to understand a particular account within a regional ecosystem, across all periods of the contract or for a particular period –

What has been the number of gross sales? Has that gone up or down compared to its last quarter?
What part of the total gross sales is contracted vs non-contracted? What part of sales is attributed to specialty pharmacy? What percentage of account savings is attributed to contracting?

Also, another key insight to look at for portfolio purchasing summary for the account is to identify the product mix/segment mix (GPO, 340B, NCCN, etc.) across the portfolio, and double click on which product/ segment contribution has gone up, or down over the previous periods, and how does it fare against its anticipated baseline numbers.

These insights help field teams understand account purchase volume, savings from contracted products, account performance compared to expectations, and identify opportunities for cost-saving purchases.

How to generate the pull through business insights from data?

To arrive at these insights- the key data elements that must be looked into are contracts, chargebacks, 867 Sales, non-contracted sales, rebates, terms and tiers, account hierarchy, and zip to territory-mapping data.

These data sets coming from different source systems are ingested, assimilated, and presented as output for improved decision-making –

Firstly, it needs to be ingested into cloud-based or on premise databases by using RPA tools like UIPath
The ingested data then goes through a set of data quality checks to ensure data is of expected quality.
The clean dataset then is transformed through the ETL process, where complex calculations through business-defined rules are applied, and
Presented through BI tools reports providing visualized/graphics and tabular data on gross sales, savings, performance, opportunities for an account, and other pull through insights.

Unlocking Insights with Incedo’s Data and analytics services on AWS environment

With broad and relevant expertise on data and analytics solutions on AWS cloud, Incedo offers Data and Analytics services in database transformation with data ingestion, data preparation, modernization, archival, integration with data warehouses, formation of data lakes in AWS, real time and operational analytics, business analytics, visualization and data governance. These services provide a holistic view of specific accounts in regional ecosystems, breakdown of sales components, and product/segment analysis. This empowers field teams to optimize their strategies and enhance Contract Pull Through effectiveness in the pharmaceutical industry.

AWS SageMaker provides an effective solution for creating an efficient data processing pipeline. Data was collected from various sources including contracts, chargebacks, 867 Sales, non-contracted sales, rebates, terms and tiers, account hierarchy, and zip to territory-mapping data. Once the data is ingested, SageMaker supports a set of data quality checks to verify that the data meets the expected quality standards, guaranteeing data integrity. After ensuring the data’s accuracy, it allows a transformation process involving complex calculations based on predefined rules.

Amazon QuickSight offers a powerful solution to present results through visually appealing reports and dashboards, employing Business Intelligence tools. Incedo’s strategic approach leveraged these capabilities to empower stakeholders in the pharmaceutical industry. This enables them to make informed, data-driven decisions and optimize their Contract Pull Through strategies effectively. With Amazon QuickSight, complex data is translated into comprehensible visual insights, facilitating better decision-making and ultimately enhancing the pharmaceutical industry’s operational efficiency.

Conclusion:

BEMs/ HDs pay significant attention to generating Contract Pull Through insights. Thus, large and mid-sized pharmaceutical companies should invest in a robust system to understand account performance, optimize rebates, and potentially save millions of dollars. This also aids in focusing on top accounts and renegotiating terms for underperforming ones.

In today’s fast-paced business landscape, where efficiency, accuracy, and cost savings reign supreme, organizations are increasingly turning to automation to streamline their financial operations. According to the Institute of Financial Operations & Leadership, only 9 % of Accounts Payable (AP) departments are fully automated today. However, – the Strategic Treasurer survey reveals that, by 2025 67%s of finance professionals anticipate their AP departments will be entirely automated. This transformation is driven by an emphasis on cost savings, with 73% of organizations stating that it is the primary motivator for shifting to fully electronic processing. This shift bolsters both an organization’s operational efficiency as well as its financial health.

In this blog, we will explore why and how automating the Accounts Payables process is vital and delve into the myriad benefits of IncedoPay in achieving this transformation. Further, we will elaborate how IncedoPay eliminates the need for upfront enterprise investments in infrastructure, hosting fees, and licenses. It addresses critical customer challenges by reducing capital expenditure (Capex) and operational costs, thanks to its deployment on the AWS cloud and its service model based on per-payment transaction costs.

The Need for AP Automation: The traditional AP process is manual and resource intensive, – involving tasks such as data entry, invoice verification, purchase order matching, and physical check handling. This manual approach is both time-consuming and error-prone, leading to delayed payments, inefficiencies, supplier frustrations and potential compliance issues. AP Automation can help overcome these challenges by:

Streamlined Workflow: Automation of the AP process facilitates a seamless and well-structured workflow. Purchase orders & invoices are electronically captured, verified, and routed through approval workflows, reducing the risk of errors and fraud. This expedites payment cycles and enables organizations to take advantage of early payment discounts, optimizing cash flow.
Enhanced Visibility and Control: Automated AP systems provide real-time visibility into financial data, offering better tracking of financial commitments. This transparency facilitates more informed decision-making about cash flow and liabilities while ensuring compliance with financial regulations and reporting requirements.
Cost Reduction: Manual AP processes incur hidden costs, of which labor costs constitute the most significant portion. Automation eliminates these expenses, resulting in significant cost savings. Improved cash flows with visibility and control over the liquidity allows organizations to not only reduce costs but to enhance forecasting and spend management.
Vendor and Supplier Relations: Timely and accurate payments foster positive relationships with vendors and suppliers. Automated AP systems streamline the processes effectively to ensure that payments are made promptly and accurately, reducing the risk of disputes and enhancing trust among business partners.

IncedoPay – Revolutionizing the Accounts Payable Landscape for Banks and Enterprises:

IncedoPay, a proprietary payments platform from Incedo Inc., empowers enterprises to automate the process of Accounts Payables (AP) by integrating multiple payment systems while maintaining control, governance, and payment approval. IncedoPay stands as a beacon of innovation and excellence in the realm of integrated payment solutions, tailored specifically for banks and their corporate customers. As a best-in-class platform, IncedoPay harnesses the power of automation to revolutionize every facet of the Accounts Payable (AP) lifecycle, elevating profitability, productivity, and the overall user experience.

IncedoPay redefines the banking experience by seamlessly integrating digital solutions. With self-service portals catering to various stakeholders (eg. bank and their corporate customers) and personalized payer & payee journeys, to offer unmatched convenience to users. Real-time visibility and payment tracking further enhance the overall user experience. The platform streamlines multiple payment methods, including RTP, ACH, PayPal, Zelle, virtual cards, checks, and prepaid cards onto a user-friendly platform. Through the consolidation of legacy systems, IncedoPay enhances user experiences, transforming the way businesses handle their financial transactions.

IncedoPay isn’t just a payment platform; it’s an innovation-led, secure, and efficient payment solution. It adheres to the most stringent industry standards. It empowers banks with proactive fraud reduction and regulatory compliance management by analysing transactional data such as vendor details, payment amount and terms etc to identify anomalies or suspicious activities. This includes sentiment analysis and transactional data analytics. Replacing legacy applications, boosts business performance and unlocks upsell/cross-sell opportunities through data-driven decision-making.

Elevating Payments with Innovation, Security, and Reliability with AWS

IncedoPay is powered by Amazon Web Services (AWS), a powerhouse in the realm of cloud computing. AWS empowers IncedoPay to redefine the digital payment landscape, offering high-performance, secure, and scalable solutions. In the online payment world, security is paramount. AWS Key Management Service ensures the security and compliance of IncedoPay’s data. Web Application Firewall stands as the first line of defense, guarding against threats and ensuring uninterrupted service. Combining Availability Zones, Auto Scaling, and Elastic Load Balancing, ensures business continuity and cost-efficiency. Elastic Kubernetes Service (EKS) allows IncedoPay to swiftly adapt to market demands and Relational Database Service (RDS) enhances resilience, scalability, and compliance. The platform optimizes performance through caching and continuously monitors and optimizes resources with Amazon CloudWatch.

With global supply chains, late payments, evolving regulations, uncertainties, and cybersecurity threats, risk and compliance management become paramount. IncedoPay adheres to the most stringent industry standards. It empowers banks with proactive fraud reduction and regulatory compliance management through the use of information, sentiment analysis, and transactional data analytics.

IncedoPay’s remarkable impact is clearly demonstrated through the key statistics derived from its wide range of customer deployments:

Cost Reduction: IncedoPay has achieved substantial cost savings, with one of our customers experiencing an astounding 80% reduction in processing costs. Another one realized an impressive 37% reduction in operations costs.
User Adoption: IncedoPay’s modular architecture, coupled with tailored campaigns, has significantly boosted user adoption rates. In one instance, digital payment usage soared by 2x, while new supplier outreach increased sixfold. The platform streamlines the process of onboarding new suppliers, capturing their payment details, alerting them about payments, and providing regular metrics on their business relationships.
Enhanced Efficiency: The platform’s seamless integration and comprehensive payment options have streamlined legacy processes and significantly reduced processing times. This improvement is evident in the 70% reduction in support requests from suppliers experienced by one of our customers, enhancing business cohesiveness.

Automation is critical for organizations to optimize cash flow, reduce costs, enhance financial control, and strengthen relationships with key stakeholders. Embracing this change enables businesses to thrive in an increasingly competitive financial landscape. So don’t wait; start your journey toward a more efficient and prosperous financial future today.

The explosion of data is a defining characteristic of the times we are living in. Billions of terabytes of data are generated every day, and million more algorithms scour this data for patterns for our consumption. And yet, the more data we have, the harder it becomes to process this data for meaningful information and insights.

With the rise of generative AI technologies, such as ChatGPT, knowledge workers are presented with new opportunities in how they process and extract insights from vast amounts of information. These models can generate human-like text, answer questions, provide explanations, and even engage in creative tasks like writing stories or composing music. This breakthrough in AI technology has opened up possibilities for knowledge workers.

Built with powerful LLMs, Generative AI has taken the world by a storm and led to a flurry of companies keen to build with this technology. It has indeed revolutionized the way we interact with information.

And yet, in this era of ever-increasing information overload, the ability to ask the right question has become more critical than ever before.

While the technology has evolved faster than we imagined, its potential is limited by the ways we use it. And while there is scope for immense benefits, there is also a risk for harm if users don’t practice judgment or right guardrails are not provided when building Gen AI applications.

As knowledge workers and technology creators, empowering ourselves and our users relies heavily on the ability to ask the right questions.

Here are three key considerations to keep in mind:

1. The Art of Framing Questions:

To harness the true potential of generative AI, knowledge workers must master the art of framing questions effectively. This involves understanding the scope of the problem, identifying key variables, and structuring queries in a way that elicits the desired information. A poorly constructed question can lead to misleading or irrelevant responses, hindering the value that generative AI can provide.

Moreover, knowledge workers should also consider the limitations of generative AI. While these models excel at generating text, they lack true comprehension and reasoning abilities. Hence, it is crucial to frame questions that play to their strengths, allowing them to provide valuable insights within their domain of expertise.

2. The Need for Precise Inquiry:

Despite the power of generative AI, it is essential to remember that these models are not flawless. They heavily rely on the input they receive, and the quality of their output is heavily influenced by the questions posed to them. Hence, the importance of asking the right question cannot be overstated.

Asking the right question is a skill that knowledge workers must cultivate to extract accurate and relevant insights from generative AI. Instead of relying solely on the model to generate information, knowledge workers need to approach it with a thoughtful mindset.

3. Collaboration between Humans and AI:

Generative AI should be viewed as a powerful tool that complements human expertise rather than replacing it. Knowledge workers must leverage their critical thinking, domain knowledge, and creativity to pose insightful questions that enable generative AI to augment their decision-making processes. The synergy between human intelligence and generative AI has the potential to unlock new levels of productivity and innovation.

Think of Gen AI as a powerful Lego block, a valuable component within the intricate structure of problem-solving. It’s not a replacement but an enhancement, designed to work in harmony with human capabilities to solve a problem.

In conclusion, in the age of generative AI, asking the right questions is fundamental. Careful framing of queries unlocks generative AI’s true power, enhancing our decision-making. Cultivating this skill and fostering human-AI collaboration empowers knowledge workers to navigate the information age and seize new growth opportunities.

Imagine starting a project from scratch – be it designing, writing, or developing new tech features; the challenge always lies in finding the starting point. The struggle can sometimes last for hours straight until you find a breakthrough. However, what if we could have a repeatable setup for such scenarios which can speed up the process? Wouldn’t that make the process much simpler? Well, yes! This is what a design system is in the world of UI and UX design.

A design system is a collection of reusable components, guidelines, and assets that are designed to help cross-functional teams create consistent and cohesive products or services. It includes a set of shared principles, patterns, and practices that provide a framework for designing and building products that are scalable, feasible, and user-friendly.

An effective design system is instrumental in driving efficiency since it serves as a framework for designing and building new products. Consequently, the focus and efforts shift towards creating high-quality products that meet the users’ needs as the redundant processes are eliminated. By having a shared understanding of design principles, styles, tokens, and patterns, teams can operate with better coordination, and make better decisions about what to include in their products. This can also help to improve communication between team members, making it easier to collaborate and share feedback.

Design systems can also help teams deliver products more quickly and with greater consistency. By providing a library of pre-built components and templates, designers and developers can focus on the unique aspects of projects, rather than spending time reinventing the wheel. This not only saves time, but also helps to ensure that the end product is more cohesive and aligned with the overall design vision.

Most importantly, design systems enable consistency and continuity in designs across a range of products, even if they are developed by different teams or individuals. Ultimately, a design system can help lead the development of brand identity while smoothening the user experience via relatable elements, making the platforms more intuitive.

Overall, design systems are a powerful tool for improving efficiency, consistency, and quality in cross-functional teams. They help to streamline the design and development process, reduce errors, and ensure that the highest standard of the product is delivered and on time.

Incedo Zeal – The design system

Incedo has developed a design system called Zeal that is both adaptable and durable. The main objective is to develop digital products that are uniform, multifunctional, and able to conform to changing trends and requirements. The system consists of essential building blocks, components, templates, and guidelines that all work together to produce a modern, sleek style that aligns with Incedo’s values and principles.

The impeccable Incedo Zeal streamlines the design and delivery process by providing reusable components leading to greater efficiency, consistency, and cost-effectiveness.

Visual Design & Strategy

The visual design language is the core of a design system. It’s made up of the distinguishable components that are used to construct a digital product. The visual design language is made up of four main categories, including color, typography, spacing & size, and imagery.

Components

Components are the foundation of a design system, serving as the essential building blocks that provides structure and consistency to every aspect of product design. By creating a library of reusable components, you can establish a visual and functional standard that permeates every product and service you offer. This systematic approach to design ensures that every user experience is intuitive, cohesive, and optimized for their needs. Patterns take this a step further by providing tested solutions for common user objectives, offering a reliable framework for designers to build upon. With a well-defined set of components and patterns, you can create a comprehensive recipe for design success that delivers consistently outstanding results across all your products and services.

Icons

One of the more significant aspects of a design system is its icons. Icons provide a fast and easy means of conveying labels, actions, and metaphors in digital interfaces. The Zeal icons feature a sleek yet friendly style, with smooth rounded corners and simple aesthetics. The Zeal icons offer flexibility and a polished visual appearance.

Content Design & Strategy

While there has been some pathbreaking development around visual design, the importance of content design & strategy has been understated for much of this time. Words are the means by which a brand communicates with its audience. The standard of communication defines the brand personality and hence, words and the form of language must be chosen carefully.

Voice & tone

Incedo’s culture is driven by diversity, equity, and inclusivity. These values are the essence of our brand personality and reflect in our voice. Our communication is driven by optimism, practicality, and sheer boldness. Our tonality promotes inclusivity in language and totally shuns any words, phrases, or tones that may express bias, stereotypical, or discriminatory views toward certain groups of people. This clarity constantly reflects in our voice and tone, enabling a consistent and authentic user experience.

Within these guidelines, we are flexible in our approach to communication, interacting with different tones and figures of speech as per the need of the platform and the several situations within the platform. Our incessant research into several digital platforms, the human psyche, and user journey and experience have enabled us with an understanding of when to be serious and direct and when to adopt a more relaxed tone. Ultimately, a top-notch user experience is at the center of our thought when designing the content.

“Our voice represents our personality, while our tone reflects our mood”

Balancing clarity and conciseness

Simplifying a user’s journey through a platform is fraught with challenges since every stage isn’t the same and requires differential treatment. Early recognition of this helps us to ensure that we are more descriptive, albeit at the cost of conciseness, in stages that are complex and difficult to understand while keeping the content very concise and direct through the screens which are simpler. We keep users informed of their current position in the journey so as to keep them from feeling lost.

Such gradient content design is brought about by being empathetic towards the user’s frustration and pain points. Encouragement through effective content strategy in form of tooltips, waypoints, and interactive walkthroughs at appropriate places encourages the user to continue and explore the platform to their benefit.

Incedo Zeal in Essence

These principles are intended for anyone at Incedo who engages in customer-facing communication, including product managers, content writers, UX designers, developers, or engineers. We adhere to our voice and tone across all our channels and properties, from in-product interfaces to error messages and menus.

We utilize these principles to inform and build trust with our users. By being transparent and clear about product experiences, we provide the necessary information without overwhelming the users with irrelevant or redundant details.

Ultimately, the Zeal design system complements the brand’s focus on simplicity, with a sleek and refined visual language incorporated throughout the UI. The design is consistent across both light and dark themes, and every aspect has been designed with careful attention to detail. The eventual outcome of this design approach is an interface that not only looks aesthetically pleasing but is also functional and serves the needs and goals of the user. Additionally, it aligns with the overall brand of Incedo and takes into consideration the user’s perspective and experience. In a nutshell, Zeal is a comprehensive solution designed to produce clean, minimalistic digital products in the Incedo ecosystem.

To say the payments industry is going through disruption is certainly not a hyperbole these days. The fundamental shifts in how commerce gets done have begun to impact the way payments have been done all these years. On the one side, the payments industry has seen the entry of diverse fintech players, including giants like Facebook and Tencent, in addition to the start-ups that are presenting increased competition for banks and corporations. On the other, the threat from fintechs is being further fuelled by rapidly evolving customer expectations, which continue to push the boundaries for the industry as a whole. It is increasingly apparent that the payments marketplace will look fundamentally different a decade from now. There will be new form factors, real-time infrastructure, greater levels of integration with social media and e-commerce, to name a few of the changes. In effect, the revolution that has completely disrupted the consumer payments industry over the last decade or so is finally coming to take into its fold the corporate payments industry, too.

I see three big trends that are likely to shake up this segment, which is at half a trillion dollars a year, and growing:

Direct-to-consumer models: As firms across industries move to direct engagement with their customers, it is becoming increasingly necessary to deliver them the same level of digital experiences as the consumer payments industry does.
Global payment flows: Cross-border payments now make up over 10 per cent of all corporate payments, and they are growing. These flows are almost always digital in nature, with the added complexity of regulatory compliance and risk management.
Data monetization: Bank treasury services have had the advantage of managing and servicing fund flows between their corporate clients. And as these fund flows become increasingly digital, they have enabled banks to build a data goldmine. Banks are now actively looking to leverage this data to deepen their service offerings.

Banks and corporations have started responding to the call of digital, and the payments processing industry is currently going through a wave of infrastructure modernization. I see significant technology investments by CIOs across firms that are setting the stage for the next wave of digital transformation. The payments industry will look fundamentally different a few years from now. By adopting digital channels, embracing automation, adopting open standards and making smart bets in technology, banks and corporations can emerge as winners in the payments marketplace.

Making digital transformation happen

As digital transformation initiatives in payments pick up steam, there are four main areas of focus, each of which is important to ensure not just a solid foundation for a digital payments ecosystem, but also to ensure the groundwork for unlocking the revenue potential from treasury and payment operations. This is something yet to be tapped in most organizations:

Enabling of digital channels: Buoyed by the consumer payments industry, there is a rapidly growing array of digital payment channels that need to be integrated into the digital payments service offerings.
Process automation: Payment processes typically span across entities (bank-corporation-consumer), and integration across disparate systems will make for a critical foundation to enable scalable implementations.
Payment analytics: Payment processes have always been data rich, and even more so when digital channels continue to grow. Effective use of the data to make better decisions (e.g., manage risk, prevent fraud) and, furthermore, explore data monetization opportunities, are becoming important.
Adoption of standards: For any multi-entity ecosystem with entities across the globe to scale with technology, it is essential to establish standards. Adoption of open banking standards is essential for digital payments to succeed – and we are at an inflection point, given the increasing adoption of these standards.

Digital channels

The ubiquitous cheque has been the staple in corporate payments for years now. Despite being the most expensive payment instrument, the cheque has dominated the corporate payment world for decades. It is not just the processing cost of the paper cheque that makes it a burden for banks. It is also a security headache. It is well known that paper cheques are the largest vehicle of payment fraud.

All that is changing. And, as it usually happens, this started with the consumer payments business. As the direct-to-consumer models continue to evolve, the B2C payments business is growing rapidly (annual growth rate of 15 per cent led by digital e-commerce)6. And the digital payment technologies that are coming out of the consumer payments industry (Zelle, Paypal, digital wallets, et al) offer a rich choice for banks and corporates to offer digital experiences to their customers:

Disbursement of funds: Transfer volumes of Medicare/Medicaid funds by healthcare providers to their members continue to rise and should, by and large, be digital.
Refund management: In a direct-to-consumer world, corporations need to manage refunds to customers from excess payments and product returns. Customers used to instant digital payments from the e-commerce world are expecting a similar experience everywhere.
Loyalty/reward disbursements: As corporations build deep relationships with their customers, they continue to adopt customer engagement strategies from e-commerce retailers. These include cash-back payments and encashing of loyalty points, which need to be executed through digital channels. A similar revolution is around the corner in B2B payments, with the expansion of consumer-like payment rails, such as digital wallets, in addition to the existing ones like ACH, Wire, virtual cards, etc. We believe the convenience of digital payments is only a starting point. There is so much more to it by way of benefits:
Streamlining of payment processes has a direct impact on working capital management. Trade finance is key to enabling global supply chains, and fintechs are coming up with specific solutions.
Cross-border payments to suppliers and subsidiaries need to stand up to heavy regulatory requirements in addition to managing the risk of fraud. Digital payments are increasingly the safest alternative. In addition to enforcing compliance, digital channels can ensure transparency of global fund flows
Of late, acquiring a deeper understanding of the supplier ecosystem has become an important factor (see the section on payment analytics below).

Automation

70 per cent of corporate treasury and payments professionals list manual and inefficient processes among their top challenges. In addition to their high costs, manual processes are also error-prone, difficult to scale in response to variable volumes, and increasingly susceptible to fraud.

Process simplification and automation opportunities extend across the value chain – from establishing the payment exchange with suppliers (B2B) and consumers (B2C) to creating a variety of services around three-way (PO, invoice and receipt) matching, and all the way to the disbursement of funds through different digital payment channels. Several fintechs see this as a big area of opportunity and are building to this end auxiliary platforms that can integrate with corporate systems and automate the end-to-end processes.
Bank treasury services offer a slew of products and services – from cheque processing to ACH/Wire – to their corporate clients. For instance, a large bank helps one of the largest healthcare providers in the US process over 4 million transactions on an annual basis, covering their entire value chain, from providers – corporate hospitals (B2B) and individual doctors (B2C) – to pharmaceuticals (B2B).

There is a clear opportunity to digitally onboard these entities onto the payments network in a rapid, secure manner using DIY portals as well as create an ‘omni-channel’ like experience that minimizes onboarding friction.
Automating a payment network of this size and complexity is undoubtedly an integration challenge, given the multiple legacy systems at enterprises and, increasingly, ERP systems. With the increasing adoption of API (application programming interface)-based data exchanges, end-to-end automation with multiple payment rails is a necessary building block for digital payments.

Data and analytics

Payment processing through digital channels is data rich: strategies and execution led by analytics on the transaction data can help in a variety of ways: improving revenues, cutting operating costs, detect fraud and other anomalous behaviour.

Risk and fraud analytics: As payments migrate to digital platforms, it is almost inevitable that fraud becomes more sophisticated too. And, as the volume and complexity of payments grow, fraud is becoming just as hard to track, identify and prevent. Fraud prevention will have to move beyond transaction-centric assessment to leveraging AI for detection and prevention of emerging fraud. Broadly speaking, organizations need to think of payments fraud at two levels:

Account fraud: Digital identity theft is a leading cause of fraud. Methods like ATO (account takeover) and synthetic identity creation can be used to gain access into accounts and siphon funds. Tracking and preventing this requires going beyond the traditional knowledge-based authentication methods to monitor authentication journeys, looking for anomalous patterns.
Phantom payments: Businesses lose significant amounts to fraudulent payments, triggered both by employees (e.g., initiating a phantom payment) or payees (e.g., creating double invoices). Monitoring and flagging them requires a range of methods, starting from rule-based systems (e.g. proximity of transaction requests) to more sophisticated machine learning methods (e.g., payee behaviour risk-scoring and setting of guard-rails sensitive to each risk segment).

Data monetization: A bank managing the payment flows between its corporate clients and their network of suppliers and customers has the unique ability to understand the financial behaviour of all the firms in this network. This can be a powerful tool for the bank to develop targeted strategies to drive superior experience and value for its clients:

Working capital optimization: Banks can help their clients optimize their working capital by forecasting fund flows and using that to transfer the optimal funds into their payment accounts.
Service bundling: Using a combination of behavioural and transactional patterns, banks can help define optimal service bundles for their clients. For example, corporate payments that span multiple countries can be optimized with a combination of exchange-rate hedging and currency-float solutions.

Adoption of standards

Open Banking

Starting in 2015, when the European Parliament adopted open banking standards (PSD2), there has been a growing momentum in adoption of standards. And, as it happens with standards, this can catalyse innovation and efficiency across the world of payments once they reach a critical mass of adoption. Open banking regulations require banks to open up their systems and data to third-party providers through secure channels. This has the potential to accelerate:

Seamless transfer of funds between banks, using standards as opposed to relying on the current custom of point-to-point software integrations.
The capability of corporations with multiple bank accounts across currencies to efficiently aggregate bank account data into a single accounting portal for automated reconciliation, mitigating issues in one of the most complex of payment transactions – crossborder payments.

Blockchain technology

Several financial services firms are increasingly looking to blockchain technology to mitigate the risk of fraud. The three fundamental underpinnings of the technology are distributed ledger, and immutable and permissioned access. Taken together to underpin a payment processing service, they make it possible to trace the entire sequence of wire transfers. Visa launched its B2B Connect Platform based on a private blockchain with the aim of enabling faster cross-border payments. Similarly, a host of banks, including HSBC, BNP Paribas and ING, launched Contour, a blockchaininspired platform designed to make the $18-trillion trade finance market more efficient and secure. I expect this space to see a lot more action in the coming years.

After decades of plodding along with archaic systems, the $2 trillion behemoth that is the global payments industry, is waking up, shaken up by the fintechs (a revolution of sorts that PayPal ignited).12 And, as it often happens, innovation in one sector rapidly spills over to adjacent areas; the dramatic change that started in consumer payments created the technology building blocks for digital disruption in corporate payments too. Combined with the adoption of standards and, most notably, the maturity of blockchain technologies, the corporate payments industry is primed for a burst of innovation.

A Complementary Partnership

“Data is the new currency.”— has gained immense popularity in recent years as data is now a highly valuable and sought-after resource. Overtime data continues to be accumulated and is becoming increasingly abundant. The focus has now shifted from acquiring data to effectively managing and protecting it. As a result, the design and structure of data systems have become a crucial area of interest, and research into the most effective methods for unlocking its potential is ongoing.

While innovation and new ways keep coming to the fore, the best of the ideas currently consists of two distinct approaches in the form of data mesh and data fabric. Although both aim to address the challenge of managing data in a decentralized and scalable manner, they have different approaches and benefits, and they differ in their philosophy, implementation, and focus.

Data Mesh

The architectural pattern was introduced by Zhamak Dehghani for data management platforms that emphasize decentralized data ownership, discovery, and governance. It is designed to help organizations achieve data autonomy by empowering teams to take ownership of their data and provide them with the tools to manage it effectively. Data mesh enables organizations to create and discover data faster through data autonomy. This contrasts with the more prevalent monolith and centralized approach where data creation, discovery, and governance are the responsibility of just one or a few domain-agnostic team(s). The goal of data mesh is to promote data-driven decision-making and increase transparency, break down data silos, and create a more agile and efficient data landscape while reducing the risk of data duplication.

Building Blocks of Data Mesh

Data Mesh Architecture

Since data mesh involves a decentralized form of architecture and is heavily dependent on the various domains and stakeholders, the architecture is often customized and driven as per organizational needs. The technical design of a data mesh thus becomes specific to an organization’s team structure and its technology stack. The diagram below depicts a possible data mesh architecture.

It is crucial that every organization designs its own roadmap to data mesh with conscious and collective involvement of all the teams, departments, and line of Business (LoBs), with a clear understanding of their own set of responsibilities in maintaining the data mesh.

Data Fabric

Data Fabric is not an application or software package; it’s an architectural pattern that brings together diverse data sources and systems, regardless of location, for enabling data discovery and consumption for a variety of purposes while enforcing data governance. A data fabric does not require a change to the ownership structure of the diverse data sets like in a data mesh. It strives to increase data velocity by overlaying an intelligent semantic fabric of discoverability, consumption, and governance on a diverse set of data sources. Data sources can include on-prem or cloud databases, warehouses, and data lakes. The common denominator in all data fabric applications is the use of a unified information architecture, which provides a holistic view of operational and analytical data for better decision-making. As a unifying management layer, data fabric provides a flexible, secure, and intelligent solution for integrating and managing disparate data sources. The goal of a data fabric is to establish a unified data layer that hides the technical intricacies and variety of the data sources it encompasses.  

Data Fabric Architecture

It is an architectural approach that simplifies data access in an organization and facilitates self-service data consumption. Ultimately, this architecture facilitates the automation of data discovery, governance, and consumption through integrated end-to-end data management capabilities. Irrespective of the target audience and mission statement, a data fabric delivers the data needed for better decision-making.

Principles of Data Fabric

Parameters	Data Mesh	Data Fabric
Data Ownership	Decentralized	Agnostic
Focus	High data quality and ownership based on expertise	Accessibility and integration of data sources
Architecture	Domain-centric and customized as per organizational needs and structure	Agnostic to internal design with an intelligent semantic layer on top of existing diverse data sources
Scalability	Designed to scale horizontally, with each team having their own scalable data product stack	Supports unified layer across an enterprise with the scalability of the managed semantic layer abstracted away in the implementation

Both data mesh and data fabric aim to address the challenge of managing data in a decentralized and scalable manner. The choice between the two will depend on the specific needs of the organization, such as the level of data ownership, the focus on governance or accessibility, and the desired architecture.

It is important to consider both data mesh and data fabric as potential solutions when looking to manage data in a decentralized and scalable manner.

Enhancing Data Management: The Synergy of Data Mesh and Data Fabric

A common prevailing misunderstanding is that data mesh and data fabric infrastructures are exclusive to each other i.e., only one of the two can exist. However, fortunately, that is not the case. Data mesh and data fabric can be architected to complement each other in a way that the perquisites of both technologies are brought to the fore to the advantage of the organization. 

Organizations can implement data fabric as a semantic overlay to access data from diverse data sources while using data mesh principles to manage and govern distributed data creation at a more granular level. Thus, data mesh can be the architecture for the development of data products and act as the data source while data fabric can be the architecture for the data platform that seamlessly integrates the different data products from data mesh and makes it easily accessible within the organization. The combination of a data mesh and a data fabric can provide a flexible and scalable data management solution that balances accessibility and governance, enabling organizations to unlock the full potential of their data.

Data mesh and data fabric can complement each other by addressing different aspects of data management and working together to provide a comprehensive and effective data management solution.

In conclusion, both data mesh and data fabric have their own strengths but are complementary and thus can coexist synergistically. The choice between the two depends on the specific needs and goals of the organization. It’s important to carefully evaluate the trade-offs and consider the impact on the culture and operations of the organization before making a decision.

What is Contract Pull Through?

Now, the current size of pharma gross contracted sales is to the tune of 50B USD and projected to grow to 85B USD over the next 5 years^[1], making this a big area for the pharma commercial team to have a close look and improve sales effectiveness while engaging with these accounts. Pharma companies are interested in the contracted sales, rebates, terms, and tiers data from the accounts to measure effectiveness. Mainly to figure out which of the existing accounts are underperforming, and which are performing above benchmark – and this aspect of pulling contract data across accounts to measure account effectiveness is the key objective of Contract Pull Through.

We may define Contact Pull Through, as the analysis of –

How much an account has purchased (sales) by contract program and by brand;
How much they’ve received in discounts (rebates, chargebacks);
How they are doing against their baselines; and
Where are the opportunities to buy more and save more?

Why does pharma need to focus on Contract Pull Through?

Generating contract awareness and pull through for major providers in their ecosystems
Creating awareness around the contracts/terms offered by pharma firms for the products
Engaging with customers to show their historical performance and current performance

Some specific Contract Pull Through use cases the pharma account team focuses on include –

A portfolio purchasing summary of the account, which enables the account to understand how much volume the account has bought, and the savings received from pharma products
Product contribution at an account level, which enables the understanding of how much volume is coming from each pharma product
Contract eligibility of an account, to understand which contracts are available at a certain account

Other business insights that Contract Pull Through data may help pharma companies are around –

How is the account performing as compared to others, in their ecosystem/region?
Which is the account’s dominant payer, and how does that payer work at a national level?
How much this account can purchase to reach the next tier?

Use case deep dive: Portfolio purchasing summary of an account

What has been the number of gross sales? Has that gone up or down compared to its last quarter?
What part of the total gross sales is contracted vs non-contracted? What part of sales is attributed to specialty pharmacy? What percentage of account savings is attributed to contracting?

Insights of this degree can immensely help the field team members understand how much volume the account has bought and the savings received from contracted pharma products, how are the accounts performing against their expectations, and where there are opportunities to buy more to save more.

How to generate the pull through business insights from data?

These data sets coming from different source systems are ingested, assimilated, and presented as output for improved decision-making –

Firstly, it needs to be ingested into cloud-based or on-prem databases by using RPA tools like UIPath
The ingested data then goes through a set of data quality engine checks to ensure data is of expected quality.
The clean dataset then is transformed through the ETL process, where complex calculations through business-defined rules are applied, and
Presented through BI tools reports providing visualized/graphics and tabular data on gross sales, savings, performance, opportunities for an account, and other pull through insights.

Conclusion: CPT – Top of mind for strategic and financial objectives

BEMs/ HDs have Contract Pull Through insights generation as a huge part of their mind share. Thus, large and mid-pharma organizations are, or should invest in building a robust Contract Pull Through system to enable them to understand how the accounts are performing against their expectations, and in turn, identify the opportunities for them to sell more to optimize rebate payments. By doing that they have financial benefits to the tune of millions of dollars in terms of optimized rebates, and savings. As also the strategic incentive to understand the top accounts to concentrate on, and the key underperforming accounts to re-negotiate the contracting terms and tiers.

¹ U.S. Pharmacy Market Size, Share & Growth | Forecast [2028] (fortunebusinessinsights.com)

Self-service AI refers to the intelligence that business users (analysts and executives) can acquire on their own from the data without the extensive involvement of data scientists and engineers. It means enabling them to acquire the actionable intelligence to serve their business needs by leveraging the low-code paradigm. This results in reduced dependency on other skills such as core IT and programming and makes faster iterations possible at the hands of business users.

As the data inside and outside of the organizations grows in size, frequency and variety, the classical challenges such as hard-shareability across BUs, lack of single-ownership and quality issues (missing data, stale data, etc.) increase. For IT teams owning the data sources, this becomes an additional task to ensure provisioning of the data in requisite format, quality, frequency and volume for ever-growing analytics needs of various BU teams, each having its own request as a priority request. Think of the several dashboards floating in the organizations created at the behest of various BU teams, and even if with great effort they are kept updated, it is still tough to draw the exact insights that will help take direct actions based on critical insights and measure their impact on the ground. Different teams have different interaction patterns, workflows and unique output requirements – making the job of IT to provide canned solutions in a dynamic business environment very hard.

Self-service intelligence is therefore imperative for organizations to enable their business users to take their critical decisions faster every day leveraging the true power of data.

Enablers of self-service AI platform – Incedo Lighthouse^TM

Incedo Lighthouse^TM is a next-generation, AI-powered Decision Automation platform targeted to support business executives and decision-makers with actionable insights generation and their consumption in daily workflows. Key features of the platform include:

Specific workflow for each user role: Incedo Lighthouse^TM is able to cater to different sets of users, such as business executives, business analysts, data scientists and data engineers. The platform supports unique workflows for each of the roles thereby addressing specific needs:
- Business Analysts: Define the KPIs as business logic formulations from the raw data, also define the inherent relationships present within various KPIs as a tree structure
- Data Scientists: Develop, train, test, implement, monitor and retrain the ML models specific to the use cases on the platform in an end-to-end model management
- Data Engineers: Identify the data quality issues and define-apply remediation across various dimensions of quality, feature extraction and serving using online analytical processing as a connected process on the platform
- Business Executives: Consume the actionable insights (anomalies, root causes) auto-generated by the platform, define action recommendations, test the actions via controlled experiments and push confirmed actions into implementation
Autonomous data and model pipelines: One of the common pain points of the business users is the slow speed of data to insight delivery and further on to action recommendation, which may take even weeks at times for simple questions asked by a CXO. To address this, the process of insights generation from raw big data and then onto the action recommendation via controlled experimentation has been made autonomous in Incedo Lighthouse^TM using combined data and model pipelines that are configurable in the hands of the business users.
Integrable with external systems: Incedo Lighthouse^TM can be easily integrated with multiple Systems of Record (e.g. various DBs and cloud sources) and Systems of Execution (e.g. SFDC), based on client data source mapping.
Functional UX: The design of Incedo Lighthouse^TM is intuitive and easy to use. The workflows are structured and designed in a way that makes it commonsensical for users to click and navigate to the right features to supply inputs (e.g. drafting a KPI tree, publishing the trees, training the models, etc.) and consume the outputs (e.g. anomalies, customer cohorts, experimentation results, etc.). Visualization platforms such as Tableau and PowerBI are natively integrated with Incedo Lighthouse^TM thereby making it a one-stop shop for insights and actions.

Incedo Lighthouse^TM as self-serve AI at a Pharmaceutical Clinical Research Organization (CRO)

In a recent deployment of Incedo Lighthouse^TM, the key user base is the Commercial and Business Development team of a Pharma CRO. The client, being a CRO, had drug manufacturers as its customers. The client’s pain point revolved around the low conversion rates leading to the loss of revenue and added inefficiencies in the targeting process. A key reason behind this was the wrong prioritization of leads that have lower conversion propensity and/or have lower total lifetime value. This was mainly due to judgment-driven, ad-hoc and simplistic, static, rule-based identification of leads for the Business Development Associates (BDA) to work on.

Specific challenges that came in the way of application of data science for lead generation and targeting were:

The raw data related to the prospects – using which the features are to be developed for the predictive lead generation modeling – were lying in different silos inside the client’s tech infrastructure. This led to inertia to develop high-accuracy, predictive lead generation models in the absence of a common platform to bring the data and models together.
Even in a few exceptional cases, where the data was stitched together by hand and predictive models built, the team found it difficult to keep the models updated in the absence of integrated data and model pipelines working in tandem.

To overcome these challenges, the Incedo Lighthouse^TM platform was deployed that allowed them to:

Combine all the data sources’ information into a Customer-360-degree view, enabling the BDAs to look at a bigger picture effortlessly. This was achieved by pointing the readily available connectors within the Incedo Lighthouse^TM platform to the right data sources, and establishing data ELT pipelines that are scheduled to run in tandem with the data refresh frequency (typically weekly). This allowed the client’s business analysts to efficiently stitch together various data elements, that were earlier lying in silos, in a self-serve model and include custom considerations that are region and product specific during the data engineering stage.
Develop and deploy AI/ML predictive models for conversion propensity using Data Science Workbench which is part of the Incedo Lighthouse^TM platform, after developing the data engineering pipelines that create ‘single-version-of-the-truth data’ every single time raw data is refreshed. This is done by leveraging the pre-built model accelerators for predictive modeling, helping the BDAs sort those prospects in the descending order of their conversion propensity, thereby maximizing the return on the time invested in developing them. The Data Science Workbench also helped with the operationalization of various ML models built in the process, while connecting model outputs to various KPI Trees and powering other custom visualizations.
Deliver key insights in a targeted and attention-driving manner to enable BDAs to make most of the information in a short span of time. This is achieved through well-designed dashboards to rank-order the leads based on the model reported conversion propensity, time-based priority and various other custom filters (e.g. geographies, areas of expertise). The intuitive drill-downs were encoded using the region-specific KPI Trees to enable them to know the exact account portfolios of their business that were lagging behind. These KPI Trees were designed by the client’s business analysts within the platform’s self-serve KPI Tree Builder, saving multiple iterations with the IT teams. The KPI Trees allowed the BDAs to double click on their individual targets, understand the deviations from actuality, and review the comments from earlier BDAs who may have been involved, to decide the next best actions for each lead.

The deployment of Incedo Lighthouse^TM not only brought about real improvements in target conversions, but also helped transform the workflow for the BDAs by leveraging Data and AI.

The global financial services industry has seen disruption over the past few years with new fintech players and digital giants eating up into the market share of the legacy banking institutions. Whether it is digital credit lending platforms, payment tools or new credit card issuers, the common narrative to drive user adoption and build market share is enhanced customer experience through personalization.

The Value of Personalization for Retail Banking

There has been a sudden shift in the way some of the banks are leveraging personalization across the customer lifecycle. The key focus areas and objectives to enhance customer experience and deliver incremental impact for the bank are mentioned below :

Building a Growth Engine to capture new markets and customer segments
The next gen fintech players have been able to create data-enabled products that enable faster underwriting by using the prospect’s bureau scores, digital KPIs, social media data etc. to identify the creditworthiness of a prospect. The big banks need to move fast to enable similar, faster turnaround times for loan fulfillment to enable higher acquisitions from new customer segments.
Maximizing customer lifetime value for existing customer base
In order to maximize the share of customer’s wallets, banks have the following levers – improved cross sell, better servicing & higher retention rates. The best-in-class firms are leveraging AI-enabled, next best action recommendations to identify what products should be offered to a customer at what time and through what channel. The focus has also moved from reactive retention interventions to proactive, data-enabled retention strategies personalized to each customer.
Improved risk assessment and mitigation controls
Use of Personalization is not limited to marketing interventions, the digital world needs personalized controls when it comes to risk management, fraud detection, anti-money laundering and other control processes. Sophisticated AI models for risk, fraud & AML detection coupled with real time implementation is critical to build strong risk defense mechanisms against fraudsters.

The overall impact of personalization in Retail banking is dramatic with added opportunity to improve experiences across the customer touchpoints. Based on Incedo’s deployment of solutions for banking and fintech clients, given below is an illustrative example of use cases and potential opportunities.

Building a personalization engagement engine requires integrated capabilities across Data, AI/ML and Digital Experiences

The reimagined customer engagement built on personalization requires a clear understanding of customer’s needs, behavior, requirements etc. and the ability to integrate these with customer front end platforms and channels (website, mobile app etc). This requires capabilities interwoven across the spectrum of Data, AI/ML and Digital Experiences.

Data foundation and the strategy to enable a 360 degree view of the customer:
Most of the banks struggle to stitch together a holistic profile of the customer in terms of their products, lifestyle behavior, transactional patterns, purchase history, offers used, preferred channels, digital engagement etc. This necessitates development of a clear data strategy right from capturing customer touchpoints to building 360 degree data lakes for the customer. Given the huge data storage requirements, this may also mean building modern digital cloud platforms that capture not only customer’s purchase history but also granular data points like digital clickstream data.
AI, ML and Analytics-enabled decisioning layer to drive Next Best Action Recommendations:
Identifying the right product/offer/service for the customer at the right time and through the right channel of engagement is important to ensure it converts into an optimal experience for the customer. This is done through a series of AI models, customer segmentation, optimizations etc. These AI/ML models are built on historical data and are tested, monitored and enhanced on an ongoing basis to ensure any new feedback is incorporated into the models.
Building an AI/ML engine needs expert Data Scientists and Business Intelligence experts with a background in ML, statistics and contextual domain knowledge.
Optimal Digital Experience to capture customer attention and maximize conversions:
Data-enabled recommendations do not work if not supplemented with the right creatives and simplified communication that drive call to action for the customer. Digital Experiences that enable impactful omnichannel journeys whether through email, website, mobile app, ATMs, branches, service reps etc. are very important in this regard. The A/B testing of digital experiences that may include application forms, digital journeys, website interstitials, campaign banners etc. is critical to build best-in-class customer experiences.

While data, AI and digital experiences are the three key building blocks of the personalization enabled engagement layer, there is a need to orchestrate and integrate these capabilities to ensure that banks are able to deliver value from the personalization initiatives. Building these capabilities is no mean task and can take long time cycles to reap any tangible benefits, especially in cases where firms are building these capabilities from scratch.

How to turn the personalization opportunity into reality ?

While many traditional banking institutions have tried to build personalization enabled engagement, it has been observed that either the efforts fail or do not scale up over time, leading to non-optimal ROI on investments made.

Apart from building capabilities across data, AI/ML and digital experiences, it is critical to embed the personalization recommendations into enterprise users’ workflows whether it is a part of CRM, Salesforce, Credit risk decisioning systems etc. The end-to-end decision automation of workflow is critical to drive adoption and actual implementation of personalized experiences for customers.

Incedo’s Lighthouse^TM enabled CX personalization for the banks is an enterprise grade solution that enables shorter time to market for Data/AI enabled marketing personalization and accelerated realization of personalization opportunity.

Incedo’s Lighthouse^TM enabled CX personalization solution for the banks enables automated AI/ML enabled decisioning right from the data layer to customer reach out, to ensure personalized product, offer or service is delivered to the customer when it matters. The prebuilt library of Customer 360 degree data lakes and AI/ML models enable accelerated implementation of personalization initiatives. This is supplemented with digital command centers and on-a-click of a button operationalization of recommendations to deliver omni-channel engagement.

No matter where the banking clients are in their personalization journey, the solution is implemented in a way that it helps realize the business impact within a matter of weeks and not years. The solution implementation is supplemented with a personalization roadmap for the organization, where Incedo’s team of experts work together with client teams to not only implement solutions but also help the firm build its in-house personalization capabilities over a period of time.

It is critical for banking institutions to acquire new customers, maximize customer value and retain their best customers at a greater speed and accuracy then ever before. Right from personalized account opening experience to hyper-personalized cross sell product and offer recommendations to trigger-based retention strategies, providing a “wow” experience to the customer needs personalization capabilities. Complementing the trust that traditional banks and credit unions have with these capabilities would ensure that they continue to maintain their competitive advantage over the new fintech players or digital giants.

In today’s world, where the evolution of digital technology, AI and machine learning algorithms has influenced human lives, the concept of AI-driven, personalized experiences across customer touchpoints with the business has been gaining traction for some time.

“85% of businesses say they are providing somewhat personalized experiences to customers, and 60% of consumers agree with that.” – Twilio Segment Report

“72% of customers rate personalization as ‘highly important’ in today’s financial services landscape” – Capco research report “Insights for Investments to Modernize Digital Banking”

The application of personalization is becoming ubiquitous now – from the kind of articles that search sites show us, to the posts and reels that come in front of us on social media, to the kind of products that get recommended to us everywhere on the Internet. Personalized recommendations have become the smart marketer’s greatest tool and weapon for reaching out to their customers and creating a differentiation from the competitors.

For early adopters of personalization in the banking sector, the focus today is on investing in increasingly better and faster ways of personalization. Personalization today combines features bank customers want and are willing to pay for as inspired by digital banking with the human touch that still remains vital for effective customer engagement. Some of the banks, however, are still new in the journey and trying to formulate the strategy of AI-driven personalization.

Below we discuss some of the avenues where the CMO’s office has been able to unleash the power of AI-driven personalization and reap huge benefits from it.

1. Personalized Product & Service Recommendations

Retailers have been using personalization extensively to sell to and engage with their customers better. While e-tailers pioneered this space, we are starting to see companies across Banking, Telecom, FMCG & Electronics sectors, etc. using the power of personalized recommendations to enable their cross-selling and up-selling campaigns.

Banks look at factors like demographics, income & employment, transactional activity levels, spending patterns, debt worthiness & repayment, etc. to build a 360-degree view of their customers. Using this intricate knowledge of their customers’ financials and spend behavior, they are able to create extremely personalized offers aimed to provide the customers with the right financial tools suiting their lifestyle and needs. The level of precision helps the banks not just attract customers better, but also trim down the costs of traditional mass-reach channels like call centers.

Wealth Management firms are using similar techniques as well. As part of a more services driven business, these firms are helping financial advisors cater to investors with more personally tailored advice. Based on the knowledge of the investor’s behavior and life goals, advisors get access to the recommendations around the next-best-action to take to their clients. Also, by understanding the advisors themselves, the WM firms are able to offer them a suite of services more aligned with the advisor’s personal style of research and portfolio building.

2. Personalized Marketing Communication

Not only are companies able to tailor their products and services, but they are also able to personalize the way they communicate these to their customers. Measuring the effectiveness of past campaigns on customers, marketers can tweak some of the below levers for marketing personalization:

Messaging: Depending on the buyer segment, the messaging for a specific product can be focused on offering discounts for the discount-diggers OR for providing detailed product features for the heavy-research users OR for product comparisons for the more flexible early-stage users, etc.

Channel Personalization: Using Channel Affinity models, marketers run focus campaigns targeting the right customers on the right channels. Banks, for example, target customers having high lifestyle spends with credit card display ads on shopping sites. At the same time, the HNI customers get offered a more personal touch with the relationship managers calling them with special customized offers.

Communication Time personalization: Knowing a customer’s travel search history, Telecom companies can offer international roaming plans at the perfect time. Banks also use this strategy to offer instant lines of short-term credit to customers with a low account balance. Also, in general, based on an understanding of when a customer is most active on social media or their smartphones, marketers can run social media campaigns or send notifications to the customer for maximum impressions.

3. Personalized Digital Experiences

Beyond trying to influence the buying behavior directly, the most effective form of personalization to be offered is to update the way customers engage with the firm on a regular basis. By knowing what customers are doing on the company websites, the marketers can get a far deeper understanding of the customers’ needs and expectations. This particular vision led the CMOs to realize the extremely inadequate digital behavior tracking that companies have on their own portals and applications. What followed was a surge in digital data collection platforms like Google Analytics and Adobe Analytics. A few companies have managed to deploy these solutions effectively to understand their customers better than ever. This led them to build models for Journey Personalization, which aimed at providing the customers with the fastest path to conversions based on their interests and preferences.

Businesses that have managed to leverage the power of personalization, have consistently been able to create a differentiated positioning from their competition. This has allowed them not only to attract customers away from competitors but also command a premium price at the same time. The clear business advantage has led them to invest heavily in more use cases and enhance their models from good to great.

Incedo Lighthouse^TM – A platform to natively support personalization use cases

With our proprietary platform Incedo Lighthouse^TM, we help clients successfully deploy multiple use cases for AI-driven customer personalization. The platform brings together Big Data (millions of customers, daily updated, across several dimensions), data engineering and data science in an efficient use-case centric manner in self-serve mode. The platform can serve multiple use cases for personalization together, e.g. cross-sell offers with the right channel for right customer cohorts at specific times of the year. This leads to faster and automated implementation of the journey:
– From data to critical insights: e.g. identification of cohorts of customers that would respond to deep discounts
– And, From insights-to-actions recommendations e.g. evaluating statistically the required level of deep discounting to optimize ROI

Significant success of AI-driven, personalized recommendations has not come without its fair share of speeding tickets. Couple of examples include:
– Compromising Personally Identifiable Information (PII) inside the machine learning lifecycle, thus jeopardizing customers’ privacy
– Inadvertently introducing biases into the recommendation algorithms, leading to discrimination and unfair business practices

Incedo Lighthouse^TM helps in protecting against these issues in a very direct manner – more on that in the next blog!

Wealth management industry is transforming rapidly as it pivots towards the fee based advisory model. The advisory model by nature requires a deeper level of relationship with the customers as compared to the commission based model which is more transactional in nature. At the same time, wealth managers are facing challenges from

Changing client mix and expectations,
Fee compression
Fintech disruption.

You can read details about how digital disruption is shaping the wealth management industry in our previous blog. Investment management has been commoditized and is no longer a differentiator. A robo advisor can perform portfolio allocation much better and at a much lower cost than a human advisor. If the advisors expect to charge more than robo advisory fee then they need to offer personalized financial advice based on holistic understanding of the client’s life stage, risk profile, investment objectives, preferences etc.

Issues with Traditional Segmentation Methods

The foundation of all personalization efforts is rooted in understanding the clients better and segmenting clients along the slices of client value, potential, demography, behavior etc. Client segmentation is informally practiced at wealth management firms but tends to suffer from some limitations

Static segmentation – Client segmentation is not a one time product purchase, it is a continuous and dynamic process. Customers move from one segment to another over a period and their investment preference, risk profiles can change based on their own life events or market conditions. For example, in the current zero interest environment there has been more demand for the riskier instruments even from the conservative investor segments. Traditional or one time segmentation tools are not able to consider drifts over a period and can therefore provide stale results.
Just focused on Value – Every practice or financial advisor at an informal level knows their most valuable clients as measured by assets under management or fees/ commissions earned. But value-based segmentation only provides you descriptive inputs about the advisory practice. It ignores other key parameters that can help in personalizing investment decisions, service models etc. Example: life stage has direct correlation with investment recommendation eg. 529 plans for mass affluents with young kids or IRA rollover recommendations for pre-retirees.
Not scalable – Informal or semi automated segmentation methods have trouble in scaling when the number of clients increase and segmentation variables multiply. Traditional segmentation models can place clients in one segment or another but tend to provide mixed results when the number of variables and data points increase. On an average an advisor has about 80-100 clients. If we are talking about a large advisory practice with multiple advisors or ensembles with a shared servicing model, it is not possible to keep track of all clients and their changing variables without automating it.
Limits personalization – The end objective of segmentation is not just to place clients in one bucket or another, it needs to inform the decision making process for personalized next best action. A static or non automated segmentation process stays mostly at macro level. To personalize client recommendations, micro segments need to be created and the manual segmentation methods struggle with that objective. Example: Within the retirees macro segment, the objectives, risk profiles and investment patterns of early retirees will be different from those of late retirees. In the accumulation stage, the investment objectives of investors with kids will be different from those with double household income and no kids. Unless we create micro segments, wealth managers will continue to provide advice which may be generic and at worst non contextual.

Segmentation needs to be dynamic, scalable, micro level and should inform the Next Best Action

Growing use of ML/ Data science in Client Segmentation

Although the use of data science and machine learning is growing in the wealth management space, the industry still lags various other consumer facing industries in using the full potential of Data and AI/ML. Today, there are multiple factors which are making it easier for wealth managers to use the power of machines to build segmentation engines.

The data sources and volumes have exploded and there is much more fine grained level of client data available than ever before.
There is large body of knowledge from the experience of other industries on how ML based segmentation enables data driven marketing
Lastly, cloud now allows for unlimited compute capacity by spinning concurrent workloads to perform complex processing and data analytics at minimal costs.

Various wirehouses, BDs, RIAs, technology providers over the last few years have started using AI to drive their segmentation model and recommendation engines. Machine learning based client segmentation can create data driven clusters which may not be readily visible via manual segmentation. Machine learning algorithms can analyze multiple deterministic features and analyze their correlation to create unsupervised clusters sharing homogeneous characteristics and behavior patterns. Such clustering does not suffer from any unconscious bias stemming from informal segmentation.

A scalable machine learning based segmentation model relies on the following data types and is able to slice customer data along multiple dimensions. Some examples below:

Segmentation Type	Based on	Segment Example	Data Required
Geographic & Demographic	Location, Age, Income, Profession, gender	Urban vs Rural Millennials vs Baby Boomers	Client & Account Data
Value/ Potential Value	GDC , AUM, Type of Revenue (Fee vs Commissions), Networth	UNHW, HNW, Mass Affluent, Masses High Value Vs Low Value	Trades Date, Advisory Billing data, Positions Data
Risk Profile	Goals, Risk Profile, Return objectives, Time Horizon	Conservative vs Aggressive Investor	Suitability Data
Behavioral	Trading Frequency,& Patterns	Passive vs Active Investor	Trades Data, Positions Data, CRM
Technographic	Engagement with Applications	Technologically challenged vs Tech Savvy Clients	Portal & App Analytics ( Number of logins, time spent)

This data can also be supplemented with external data to provide additional insights which may not be apparent from the first party data. For example, first party data such as client zip location when supplemented with external census data can provide valuable information about zip affluence, education level, demographic segment etc. Similarly, held away investments and accounts data can help paint a holistic financial picture of the client and determine the advisor’s wallet share.

Client Segmentation across Customer Journey

Let us see how client segmentation aids data driven decision making and helps in improving key metrics during the client’s journey:

Client Acquisition- RIAs can align their prospecting efforts with the client segment value proposition to ensure a larger prospect funnel and higher prospect to client conversion . As per the Schwab 2020 RIA benchmarking study, the firms that adopted an ideal client persona and client value proposition attracted 28% more new clients and 45% more new client assets in 2019 than other firms. Therefore the first step is to identify your target segment and align your messaging and marketing accordingly. example

A business development campaign aimed at Pre retirees and retirees needs to focus on themes of safety & capital preservation while one focussed on young professionals will focus on themes of growth and return. Segmentation engine can identify geographic areas which are likely to have prospects that match the firm’s target segments and where a particular campaign will find most resonance
In another example, the segmentation engine can classify the leads and prospects data into specific segments by matching the lead characteristics with existing client segments. Segmentation engines can predict if a lead is likely to become a high value customer and also suggest the kind of campaign that will appeal to them.
Wealth Managers are now combining client segmentation and advisor segmentation to predict and match which advisors will best serve a prospective client based on prospect’s preferences, life stage etc

Wealth managers are now using segmentation for matchmaking between clients and advisors

Client Growth- To capture the greatest wallet share of their clients, advisors should tie the investment recommendations to the client’s demographic, psychographic and risk segmentation. We talked earlier about recommendations for 529 plans for investors with young kids and rollover recommendations for pre retirees. Some more examples on how customer segmentation engines are feeding into next best action platforms to provide contextual recommendations for clients:

Growing popularity of ESG with the younger investors or the increased sales of life insurance to the urban middle age group during pandemic are good examples of how advisors and product companies align product recommendations with client segment’s preferences.
Similarly, if the clients are more focussed on increasing their retirement savings, then recommendation around how they can contribute more than defined limits using backdoor roths will be appreciated by client
If the client is in a high tax bracket currently, then the advisor needs to recommend tax deductible IRAs while if they are going to be in higher tax brackets during their retirement, then Roth IRAs may be a better investment vehicle.
When segmenting based on the client’s browsing behavior, wealth managers can also send research reports and/or articles pertaining to sectors/ investment products that the client searches for in the portal. In addition, the portal can provide these inputs to advisors on the client dashboards for their next conversation.

As technology such as direct indexing mature further, clients will increasingly ask for customization based on their values, preference, beliefs and the wealth managers will have to offer customized portfolios at scale.

Client Servicing- Effective segmentation also helps in building a tiered service model with differentiated services to the most valuable clients and repeatable services to all other clients. Some wealth management firms craft personalized experiences for their top clients based on their hobbies and interests. Psychographic and technographic segmentation also help in devising the service channel for the clients. For example,

Clients that delegate all their investment responsibilities to their advisors and give them discretion on their accounts tend to prefer a light touch servicing model.
Clients who want high touch services and want to validate investment decisions would be more impressed by detailed research and analysis.
While a third category technically savvy clients want to have all the information , portfolio and plan details available anytime anywhere and prefer online service channels

Client Retention- A client mix skewed towards low value and unprofitable clients can encumber and advisory practice’s service levels & profitability and can put the most valuable clients at attrition risk. Many wealth managers have their bottom 50-60% of clients contributing only about 5% of their revenues and the top 20% accounting for more than 80% of the revenue. Therefore, advisors should periodically shrink to grow better. Client segmentation can help wealth managers prioritize clients for retention and for letting go based on current value, potential future value, influence potential. Ageing baby boomer population has brought on another kind of client attrition risk for advisors. As per industry studies, the financial advisors are not retained 70% to 90% of the time when the wealth transfers to the next generation. The client retention efforts in such cases therefore not only need to focus on the immediate clients but also on the next generation.

Segmentation is a growth as well as a Defensive Imperative

Thus, ML based segmentation can greatly aid data driven marketing efforts for wealth managers and lead to a higher return on the marketing dollars. It leads to measurable efficiencies in client servicing, attracting more clients and retaining high value clients. Lastly, it lays the foundation for a personalization engine for targeted recommendation, communication and servicing. While we have focused above on how client segmentation can turbocharge an advisory practice’s growth, it is also a defensive imperative for the wealth management industry. FAANGs have perfected customer segmentation and personalization to an art form and are eagerly eyeing trillions of dollars of the wealth management industry. Wealth managers would do well to weaponize their data by using the power of machines and insulate themselves against the looming threat of big pocketed disruptors.

FAANGs have perfected customer segmentation and personalization to an art form and are eagerly eyeing trillions of dollars of the wealth management industry

To achieve the full promise of ML based segmentation, data infrastructure needs to support running of segmentation models at scale. To paint a holistic client picture, wealth management firms also need to break data silos and ensure availability of high quality, harmonized and consumable client data. Our next blog will discuss the data challenges in the wealth management industry and how the modern data management techniques can help overcome these challenges. Till then Happy Segmenting.

Covid-19 and the aftermath: Impact on the industry

We are in the middle of one of the worst health crises the world has experienced in decades and COVID-19 has not only caused socio-economic disruption but also impacted nearly all sectors and geographies across the globe. Wealth management is one of the vulnerable sectors with highly correlated revenues to capital market performance. Despite recovery in capital markets in recent weeks especially in the US, many WMs have not seen their assets to pre-Covid levels as many European and Emerging markets are still much lower than pre-Covid levels .This has accentuated the pressure on revenues and calls for cost optimization & prudence on middle-back office functions.

Wealth management operations perform some of the most critical tasks including client onboarding checks, account setup, trading, asset transfers, etc. The immediate impact on operations was managing extremely high trade volumes and ensuring that critical processes continued to run smoothly. Most firms did not have business continuity and operations readiness plans for an event of this nature. Firms must therefore realize that this adversity presents an opportunity to resolve immediate priorities (BCP, automate critical and high effort tasks, etc.) and redefine longer term strategy to align with the paradigm shifts for an optimized operations framework.

Even before Covid-19, there was a paradigm shift that was already underway in Wealth Management operations and the pandemic merely exposed or amplified the need for Next generation operations transformation. Primary drivers of the shift were client expectations for personalized portfolios and changing priorities, growing regulations and need for real-time compliance reporting and increased competition from FinTech.

As an example, trade operations teams have always had pain points including manual reconciliation leading to delays in trading, lack of straight through processing, lower accuracy and increasing processing time, etc. Firms with higher operations maturity have relied on automation investments for e.g. automated settlement and reconciliation to minimize the impact of volatility due to COVID. Firms with lower maturity have had to rely on shuffling teams to manage trades, resources spending longer hours to complete daily trading and settlement.

What will it take to win in a post Covid world?

In today’s world, operations must not be seen as just ‘support’ but a mission-critical function. This is because acquisition costs can generate a compelling ROI only over the next 3-5 years when the wallet share is deepened. For deepening of wallet share, delivering a superior CX is critical which can happen only when wealth management firms can exceed advisor and client expectations.

For immediate resolutions, the firms should perform Next generation operations transformation’ strategy can help wealth management firms with automation capabilities, process mining and outsourcing to drive maturity and bring efficiencies

The framework should be built out in modular fashion for reusability.

Incedo believes for firms to emerge as winners in the long term, they must consider three key shifts in the way operations are managed and run. Next generation operations transformation’ strategy can help wealth management firms with automation capabilities, process mining and outsourcing to drive maturity and bring efficiencies.

Change the objective from cost efficiency to customer experience. An optimal and holistic client experience involves minimal manual touchpoints, fewer documentation and faster turnaround time for onboarding, account maintenance requests, asset transfers etc.
Wealth management firms should aim to “Re-imagine processes” rather than focus on ‘process standardization’. It goes beyond process standardization by mining data, deriving insights and determining best action to digitize and automate sub-standard processes
Firms need to focus on outcome driven KPIs rather than traditional transaction SLAs. Derive success metrics of the end to end process rather than measuring siloed metrics. For e.g. for client onboarding the key outcome to focus on is when the account is funded and ready for trading rather than measuring individual process steps for submission, set up, etc.

Over the last few years, firms have invested in automation and process improvement initiatives but have not been able to achieve maturity in their operations transformation journey.

We believe that these initiatives are not realizing their expected outcomes because:

Automation solutions deployed in silos instead of reviewing the overall customer journey
Firms are automating the current underlying processes as-is which could be inefficient and hence not achieving higher returns on investment
Focus on automating the product features rather than customer journey
Data & AI not collected and analyzed sufficiently to perform data driven decision making

To learn more about how to rethink your next generation operations transformation initiatives and how Incedo can partner with you in your journey, mail us at inquiries@incedoinc.com

COVID-19 pandemic has brought a significant change in the way financial advisors manage their practices, clients and home office communication. Along with the data driven client servicing platforms, smooth transition and good compensation, advisors are closely evaluating their firm’s digital quotient to provide them the service and support in times of such crisis and if not satisfied, may look out options of switching affiliation during or post this crisis.

This pandemic is not a trigger rather has provided additional reasons for advisors to continue to look out for a firm that fits better in their pursuit of growth and better client service.

2018 Fidelity Advisor Movement Study says, 56% of advisors have either switched or considered switching from their existing firms over the last 5 years. financial-planning.com publishes that one fifth of the advisors are at the age of 65 or above and in total around 40% of the advisor may retire over the next decade.

A Cerulli report anticipates transition of almost $70 trillion from baby boomers to Gen X, Gen Y and charity, over the next 25 years. Soon, the reduction in the advisor workforce will create a big advice gap, that the wealth management firms will have to bridge by acquiring and retaining the right set of Advisors

We are observing a changing landscape of advisor and client population, mounting cost pressure due to zero commission fee and the need for scalable operations. COVID-19 has further accentuated the need for the firms to better understand the causal factors for changes in advisor affiliation, to optimize their resources deployed for engaging through the Advisor life cycle. The wealth management firms are increasingly realising that a one fit for all solution may not get optimal returns for them.

Data and Analytics can help the firms segment their advisors better and drive better results throughout the advisor life cycle. Advisor Personalization, using specific data attributes can deliver contextual and targeted engagements and can significantly improve results by dynamically curating contextual & personalized experiences through the advisor life cycle.

A good data driven advisor engagement framework defines and measures key KPIs for each stage of the advisor lifecycle and not only provides insights on key business metrics but also addresses the So What question about those insights. As wealth management firms collect and aggregate data from multiple sources, they are also increasingly using AI/ML based models to further refine advisor servicing.

Let us look at the key goals or business metrics for each stage of the advisor life cycle below and see how data and analytics driven approach helps in each stage of the life cycle.

Prospecting & Acquisition

To attract and convert more high producing advisors, recruitment teams should be tracking key parameters through the prospecting journey of the advisors so that they can identify:

What is the source of most of their prospective advisors; RIA, wirehouses, other BDs
Which competitors are consistently attracting high producing advisor
What % of advisors drop from one funnel stage to another and finally affiliate with the firm
What are the common patterns and characteristics in the recruited advisors

Data driven advisor recruitment process that relies on the feedback loop helps in the early identification of potential converts, thereby balancing the effort spent on recruited vs lost advisors. It also improves the amount and quality of the recruited assets.

For example, analysis of one-year recruitment data of a large wealth management firm revealed that prospects dealing with variable insurance did not eventually join the firm due to the firm’s restricted approved product list. Another insight revealed that prospects with a higher proportion of fee revenue vs the brokerage revenue increased their GDC and AUM at a much faster rate after one year of affiliation. Our Machine Learning Lead Scoring Model used multiple such parameters and scored a recruit’s joining probability and 1-year relationship value to help the firm in precision targeting of high value advisors. These insights allowed the firm to narrow down their target segment of advisors and improved conversion of high value advisors.

Growth & Expansion

A lot of focus during the growth phase of the advisor lifecycle is on tracking business metrics such as TTM GDC, AUM growth, commissions vs Fee splits. The above metrics however have now become table stakes and the advisors expect their firms to provide more meaningful insights and recommendations to improve their practices. Some of the ways, firms are using data to enhance advisor practice are by:

Using data from data aggregators and providing insights on advisor’s wallet share and potential investment opportunities
Providing peer performance comparisons to the advisors
Providing next best action recommendations based on the advisor and client activities

For example, our Recommendation Engine analysed advisor portfolio and trading patterns and determined that most of the high performing advisors showed similar patterns in Investment distribution, asset concentration, churning %. This enabled the engine to provide targeted investment recommendations for the other advisors based on their current investment basket and client risk profile. The wealth management firms are also using advisor segmentation and personalization models based on their clients, Investment patterns, performance, digital engagement, content preference and sending personalized marketing and research content for the advisors based on their personas thus driving better engagement.

Maturity and Retention

It is always more difficult and costly to acquire new advisors as compared to growing with the existing advisor base. The firms pay extra attention to ensure that their top producer’s needs are always met. Yet despite their best efforts, large offices leave their current firms for greener pastures or higher pay-outs. The firms run periodic NPS surveys with their advisor population which indicates overall satisfaction levels of the advisors, but they do not generate any insights for proactive attrition prevention. Data and analytics can help you identify patterns to predict advisor disengagement and do targeted proactive interventions.

For example, our attrition analysis study for a leading wealth manager indicated that a large portion of advisors over the age of 60 were leaving the firm and selling out their business. This enabled the firm to proactively target succession planning programs at this age demographic of advisors. Our analysis also indicated a clear pattern of decreased engagement with the firm’s digital properties and decreasing mail open rate, for the advisors leaving the firm. Based on factors such as age, length of association with the firm, digital engagement trends, outlier detection, our ML based Attrition Propensity model created attrition risk scores for advisors and enabled retention teams to proactively engage more with at-risk advisors and improve retention.

As per a study from JD Power, wealth management firms have been making huge investments in new advisor workstation technologies designed to aggregate market data, client information, account servicing tools and AI-powered analytics into a single interface. While the firms are investing heavily in technology, only 48% advisors find the technology their firm is currently using, to be valuable. While only 9% of advisors are using AI tools, the advisor satisfaction is 95 points higher on a 1000-point scale when they use AI tools. Advisors find a disconnect between the technology and the value derived from the technology.

This further necessitates the need for personalised solutions for advisors and an AI driven Advisor personalisation platform which provides curated insights to the firms. This helps in targeted & personalized services & support to advisors through the Advisor lifecycle, enabling optimal utilization of the firm’s resources and unlocking huge growth potential.

The firms that will understand the potential of data driven decision making for their advisor engagement and will start early adoption of such tools will thrive in these uncertain times and will emerge as a winner once the dust settles.

Digital Transformation was one of the most important business trends across the wealth management circles before the unprecedented global disruption shifted all the focus towards ensuring business continuity. Recognizing the changing digital behavior, leading RIA custodians, broker dealers, TAMPS, RIAs had either embarked or were kickstarting their digital transformation journeys. The disruption caused by COVID 19 has clearly laid bare the nascent stages of digital evolution for the many wealth management players. The customer service centers are overwhelmed with increased call volumes and reduced capacities. Similarly, financial advisors are required to field multiple long calls from anxious clients who are uncertain about their investments. Low adoption of digital assets provided by broker dealers and RIAs firms may be a result of sub optimal CX or gaps in information availability. We all may be in a long period of disruption, and the firms that are not able to drive digital adoption, or continuing to remain person dependent will realize the difficulty in client servicing, let alone operational scaling. Digitalization needs to be looked at as an essential part of the wealth manager’s business continuity efforts as it ensures information availability and provides online self-service capabilities. Digital Insulation is another complementary term that offers an ambitious glimpse of future possibilities. To protect their businesses from personnel-related disruptions, organizations will need to invest digitalization and thus ensure business continuity.

Drivers of Digital Transformation

Digitalization in the wealth management was primarily driven by the following drivers:

Changing business model– The business model has been steadily shifting away from a product focused brokerage model to a relationship focused advisory model. In a study conducted by https://www.financial-planning.com/ , the consolidated commissions revenues for the top 50 Independent broker dealers have reduced in the last 5 years, while the advisory fee has increased by more than 50% in the same period. Shifting client base of advisory clients expect engagement across multiple channels and customer experience becomes paramount.
Revenue compression– Zero commissions are already a reality and were a seminal event for the industry. Revenue impact for the players will range from anywhere between 10%- 20%. Also, RIA custodians are likely to levy additional fees on the participants to cover for the lost revenues. With the fed rates likely to remain low for the foreseeable future, the revenue stream from sweep accounts will also reduce substantially further, thus accentuating revenue pressures.
Changing the age mix of client and advisors– As the wealth transfers from baby boomers to the millennials, the millennials will make up for an increasingly valuable client segment. Similarly, as the ageing advisor population retires, the new advisors will primarily be dependant on technology, largely influencing their business decisions.
Fin Tech Disruption– Advisor Fintech tools, also known as Advisor tech, have not only invaded the usual favorites domains such as CRM, financial planning, and portfolio management but have created new advisor tech segments such as mind mapping, account aggregation, forms management, social media archiving etc. 2020 T3 advisor software survey covered almost 500 different tools across almost 30 sub segments highlighting the plethora of tools available for clients and advisors.

The above drivers are creating two main needs for the wealth management players:

Need to Scale Servicing. The first two drivers (changing business model and revenue compression) are forcing wealth management players to realize the need to digitalize and gain operational scale for servicing more clients. In a study conducted by https://www.refinitiv.com/en, servicing clients was cited as the most important digital driver for the wealth management firms. The ongoing disruption will further fuel the demand for straight through client onboarding, E- account opening, digital signatures, and workflow-based proposal generation solutions. Moreover, the organizations that still depend on back office processors to open accounts and onboard clients will see an increased transition. Similarly, advisors and clients need to be provided with tools to move to a more self-service model.

Need to Scale Knowledge– The last two drivers (changing Age mix & FinTech Disruption) trends have resulted in increasing client and advisor expectations. An increasing number of clients no longer just delegate their investment decisions to advisors but also seek to collaborate and validate the investment decisions. They look for real time knowledge about their current investment and investment insights. With the prevailing uncertainty, many clients will also start demanding real time information about risk tolerance of their portfolios are and how they can quickly pivot to either protect their investments or to take advantage of any profitable bargains. The clients will naturally drift towards financial advisors who provide full-service client portals to access and monitor their investment. Similarly, advisors will drift towards firms which provide digital practice management tools and advisor self service capabilities.

The third form of scale which will become very relevant in the current disruption is Scaling digital collaboration. With Social distancing becoming the norm, in person client meetings may not be possible for some time. While advisors and clients can still talk and make video calls, current tools do not allow for collaborative discussion or presentations. Going forward, the organizations will need to invest in tools that enable online client engagement and advice delivery as a complimentary engagement channel. Software providers can study the evolution of telemedicine systems, which provide a full suite of features including video conferencing, document sharing, scheduling appointments, taking notes, as well as client history. Once client portals or CRM systems can be enhanced for Tele Advice, this alternate engagement channel is likely to grow in popularity with both clients and advisors, allowing remote collaboration and engagement.

To sum up, digitalization is the best antidote for any such future disruptions, which will be assisting wealth management firms in modernizing advice and accelerate their digitalization efforts to not only transform but to insulate their businesses. Digitalization can in fact become a vital cog of the business continuity efforts by enabling self-service, information disintermediation and collaboration.

While the reckless overextension of credit lines by lenders and banks was the root cause of the financial crisis of 2007-09 and it had the US primarily as its central point, this time the financial crisis has been caused by a virus with rapidly evolving geographical centers and covering almost the entire world. The banks though are in a catch 22 situation, they need to support the government’s lending and loan relief measures while also maintaining low credit loss rates and enough capital provisioning for their balance sheet. Effective risk management and credit policy decisioning was never as challenging for the banks as it is now in the post covid-19 world.

COVID-19 implications and challenges for banks and lending institutions

Sudden shift in risk profile of retail and commercial customers – The surge in unemployment, deteriorated cash flow for businesses, etc has led to a sudden shift in the credit profile of customers. The data that banks used to leverage before COVID might not provide an accurate picture of the consumer’s risk profile in the current times.

Narrow window of opportunity to re-define credit policies – Bank’s credit policies in terms of origination, existing customer management, collections, etc have been designed over years with a lot of rigor, market tests, design and application of credit risk models and scorecards, etc. The coronavirus has caught the bankers and Chief Risk Officers by surprise and there is a narrow window of opportunity to make changes in existing models and risk strategies. While a lot of banks had built a practice of stress testing for unfavorable macroeconomic scenarios, the pace and impact of coronavirus have been unprecedented. This requires immediate response from the banks to mitigate the expected risks.

Government relief programs like payment moratoriums – The introduction of payment holidays and moratorium programs are effective to take some burden off consumers but prevent the banks from understanding high risk customers as there is no measure of delinquency that banks can capture from existing data.

Four-point action plan and strategy to navigate through the COVID-19 crisis

Banks will need to go back to the drawing board, re-imagine their credit strategy and put in accelerated war room efforts to leverage data and create personalized risk decisioning policies. Based on Incedo’s experience of supporting some of the mid-tier banks in the US for post COVID risk management, we believe the following could help banks and lenders make a fast shift to enhanced credit policies and mitigate portfolio risk

Covid situational risk assessment – As a starting point, Risk managers should identify the distress indicators that capture the situational risk posed post Covid-19. These indicators could be a firsthand source of customer’s situational risk (e.g. drop in payroll income) or surrogate variables like higher utilization or use of cash advance facility on credit card etc. Banks would need to leverage a combination of internal and external parameters, such as industry, geography, employment type, customer payment behavior, etc. to quantify COVID based situational risk for a given customer.
Early warning alerts & heuristic risk scores based on a recent behavioral shift in customer’s risk profile – A sudden change in the financial distress signals should be captured to create automated alerts at the customer level, this in combination with a historical risk of the customer (pre-COVID) should go as a key input variable into the overall risk decisioning process. The Early warning system should issue alerts, alerting the credit risk system of abnormal fluctuations and potential stress prone behavior for a given account.
Executive Command Centre for COVID Risk Monitoring – The re-defined heuristic customer risk scores should be leveraged to quantify the overall risk exposure for the bank post COVID. Banks need to monitor the rapidly changing credit behavior of customers on a periodic basis and identify key opportunities. The rapid risk monitoring based command center should focus on risk across the customer lifecycle and various risk strategies and help provide answers to some of the following questions of the bank’s management team
- What is overall current risk exposure and forecasted risk exposure over short term period?
- How has the overall credit quality of existing customer base changed, are there any patterns across different credit product portfolios?
- What type of customers are using payment moratoriums, what is the expected risk of default of such customer segments?
- Quantification of the drop in income estimates at an overall portfolio level and how it could affect other credit interventions?
- What models are witnessing significant deterioration in performance and may need re-calibration as high priority models?
Personalized credit interventions strategy (Whom to Defend vs Grow vs Economize vs Exit) – To manage credit risk while optimizing the customer experience, banks should use data driven personalized interventions framework of Defend, Grow, Economize & Exit. Using customer’s historical risk, post COVID risk and potential future value-based framework, optimal credit intervention strategy should be carved out. This framework should enable banks to help customers with short term liquidity crunch through government relief programs, bank loan re-negotiation and settlement offers while building a better portfolio by sourcing credit to creditworthy customers in the current low interest rate environment.

The execution of the above-mentioned action plan should help banks to not only mitigate the expected surge in credit risk but also enable a competitive advantage as we move towards the new-normal. The rapid credit decisioning should be backed with more informed decision making and on an ongoing basis, the framework should be fine-tuned to reflect the real pattern of delinquencies.

Incedo with its team of credit risk experts and data scientists has enabled setting up the post COVID early monitoring system, heuristic post COVID risk scores and COVID command center for a couple of mid-tier US based banks over a period of last few weeks.

Learn more about how Incedo can help you with credit risk management.

The magnitude of the spread of the COVID-19 pandemic has forced the world to come to a virtual halt, with a sharp negative impact on the economies worldwide. The last few weeks have seen one of the most brutal global equity collapse, spike in unemployment numbers, and negative GDP forecasts. With the crisis posing a major systemic financial risk, effective credit risk management in these times is the key imperative for the banks, fintech and lending institutions.

Expected spike in delinquencies and credit losses post COVID-19

The creditworthiness of banking customers for both retail and commercial portfolios has decreased drastically due to the sudden negative impact on their employment and income. In case of continuation of the epidemic for a longer-term period, the scenarios in terms of defaults and credit losses for banks could potentially be much higher than as observed in the global financial crisis of 2008.

Need for an up-to-date, agile and analytics driven credit decisioning framework:

The existing models that banks rely upon simply did not account for such a ‘black swan event’. The credit decisioning framework for banks based on existing risk models and business criteria would be suboptimal in assessing customer risk, putting the reliability of these models in doubt. There is an immediate need for banks to adapt new credit lending framework to quickly and effectively identify risks and make changes in their credit policies

Incedo’s risk management framework for the post COVID-19 world

To address the challenges thrown up by the COVID-19, it is important to assess the short, medium and long-term impact on bank’s credit portfolio risk and define a clear roadmap as a strategic response focusing on changes to risk management methodologies, credit risk models and existing policies.

We propose a six-step framework for banks and lending institutions which comprises of the following approaches.

COVID Risk Assessment & Early monitoring Systems

Banks and lending institutions should focus on control room efforts and carry out a rapid re-assessment of customer and portfolio risk. This should be based on COVID situational risk distress indicators and anomalies observed in customer behaviour post COVID-19. As an example, sudden spike in utilization for a customer, less or no credit of salary in payroll account, usage of cash advance facility by transactor persona could potentially be examples of increasing situational risk for a given customer. In the absence of real delinquencies (due to moratorium or payment holidays facility), such triggers should enable banks to understand customer’s changing profiles and create automated alerts around the same.

Credit risk tightening measures

Whether you are a chief risk officer of a bank or a credit risk practitioner, by now you would have heard many times that all your previous credit risk models and scorecards would not hold and validate any longer. While that is true, it has also been observed that directionally most of these models would still rank order with only a few exceptions. These exceptions or business over-rides can be captured through early monitoring signals and overlaid on top of existing risk scores as a very short term plan. Customers with a low risk score and situational risk deterioration based on early monitoring triggers are the segments where credit policy needs to be tightened. As the delinquencies start getting captured, banks should re-create these models and identify the most optimal cutoffs for credit decisioning.

Personalized Credit Interventions

There are still customers with superior credit worthiness waiting to borrow for their financial needs. It is very important for banks to discern such customers from those that have a low ability to payback. To do this, banks require personalized interventions to reduce risk exposure while ensuring an optimal customer experience through data-driven personalized interventions. Banks need to help customers with liquidity crunch through Government relief programs, bank loan re-negotiation, and settlement offers while building a better portfolio by sourcing credit to ‘good’ customers in the current low rate environment.

Models Re-design and Re-Calibration

A wait and watch approach for the next 2-3 months period to understand the shifts in customer profile and behavior is a precursor before re-designing the existing models. This would enable banks to better understand the effect of the crisis on customer profiles and make intelligent scenarios around the future trend for delinquencies. There would be a need to re-calibrate or re-design the existing models. Periodic re-monitoring of new models would be a must, given the expected economic volatility for at least next 6-12 months period.

Model Risk Management through Risk Governance and Rapid Model Monitoring

There is an urgent need for banks to identify and quantify the risks emerging due to the use of historical credit risk models and scorecards through Model monitoring. While the risk associated with credit products has increased, the delinquencies have not yet started getting captured in the bank’s database due to the payment holiday period facility introduced by govt’s of most of the countries. In such a situation, it is critical to design risk governance rules for new models that may not have information related to dependent variables (e.g. delinquency) captured accurately.

Portfolio Stress Tests aligned with dynamic macro economic scenarios

Banks and lending institutions need to leverage and further build on their stress testing practice by running dynamic macro-economic scenarios on a periodic basis. The stress testing practice has enabled banks in the US to improve their capital provisioning and the COVID crisis should further enable banks across the geographies to use the stress tests to guide their future roadmap depending on how their financials would fare under different scenarios and take remedial actions.

The execution of the above-mentioned framework should ensure that banks and fintech’s are able to respond to immediate priorities to protect the downside while emerging stronger as we enter the new normal of the credit lending marketplace.

Incedo is at the forefront of helping organizations transform the risk management post COVID-19 through advanced analytics, while supporting broader efforts to maximize risk adjusted returns.

Our team of credit risk experts and data scientists has enabled setting up the post COVID early monitoring system, heuristic post COVID risk scores, and COVID command centre for a couple of mid-tier US based banks over a period of last few weeks.

Learn more about how Incedo can help with credit risk management.

The wealth management industry has gone through major changes in the past few years.

The amount of investable wealth among U.S. households has increased tremendously over the past few years and will be changing hands with wealth passed over to millennial. Over the next 25 years, Cerulli has estimated that $31 trillion will be passed on to Generation X households, while $22 trillion will be passed on to Millennials [1]. With two diverse trends – growing wealth in the hands of a younger demographic and aging investors primarily the baby boomers having complex financial goals ranging from retirement planning to long term medical care, the demand for financial advisors has also grown simultaneously. Independent financial advisors have continued to grow in terms of revenue and assets under management. The trend should continue as advisors can enjoy the flexibility and opportunity for higher income with fewer cuts to wirehouses and broker dealers. The major drawback of going independent is the lack of support for back office and administrative operations. This is where turnkey asset management providers come into the picture to help provide advisors the necessary support.

TAMPs have been helping advisors focus their attention on client needs while taking over back office and administrative support activities, including client onboarding, asset transfers, trade execution, portfolio management, trust accounting, proposal generation, and performance reporting.

Trends impacting advisors and TAMPs:

Financial advisers are experiencing an increase in demand by young professionals. These are the HENRYs (High earners not rich yet) segment with lower range assets but would like to start investing and are looking for financial guidance to keep them on the right track towards their long-term goals. [5]
“One of the biggest challenges facing investment advisory firms today is disintermediation. People can invest by themselves rather than hiring an investment professional to manage their money”. Advisors need to provide clients with an experience which is custom for their needs, shows value add and helps them invest strategically.
Technology is redefining the advisor-client experience in multiple ways. Clients now want to have access to their portfolios and performance instantly which means advisors need to share on-demand requests with a low turnaround time.
Clients expect personalized and custom services suitable to their individual risk profile and future goals. While tech savvy investors look for sophisticated digital systems, they also see value in the attention and financial experience of advisors to help them build a smart investment portfolio. As a result, advisors’ expectations are increasingly focused on technology & better investment management
‘Holistic financial planning,’ which goes beyond client set up and onboarding, changing investment strategy basis new life events, addressing multiple life goals are essential for advisor success. Advisors are therefore, looking for digital platforms which will enable them to service these needs. For example, a portfolio simulation which will help clients design different investment scenarios and view the impact of those changes on their goals can be hugely beneficial for advisors.

Strategy to address market trends:

Reimagining the client experience: To meet client expectations of personal and customized investment strategy, TAMPs need to provide advisors with digital solutions enabling them to walk clients through risk analysis, goal setup and investment strategy definition in a simple yet effective manner. Two technology offerings are key to successfully optimize the client experience – investor portal and smart portfolio generation platform. Clients value access to their portfolio and look for information beyond quarterly performance reports. An investor portal providing a 360 degree of the client accounts, progress towards goals, investment strategies and performance has become a basic requirement for many clients and therefore, advisors. A sophisticated portfolio selection tool which will recommend investment strategies basis the clients’ stage of life, their goals, major events such as receiving inheritance, retirement, marriage and their attitude towards risk and market changes will enable advisors to provide a hybrid model with a smart platform and human touch
Optimize advisor performance: The key success metric for TAMPs is growth in AUM, which is dependent on the success of advisors and their ability to acquire new clients and retain existing ones. Advisor performance analytics is therefore gaining traction and becoming increasingly relevant. Firms must leverage data analytics to derive insights from best performing advisors and provide the next best action to help them better collaborate with clients. To retain and bring in new advisors, TAMPs should review advisor experience metrics, assess CSAT wr.t. technology & operations services and continue to improve the experience through simplified back office processes and technology solutions.
Drive profitability through efficient operations: While technology platforms enable advisors to grow, efficient back office support is necessary to help independent advisors survive. Adding services to the operations portfolio will provide immense value add for advisors. While billing, trade management, statements generation are core activities, additional services such as sleeve level reporting, white labelling, custom proposal generation, trust accounting, tax loss harvesting, automated rebalancing, account aggregation will help acquire more advisors. A key focus area for TAMPs should be to minimize operations & compliance risk as meeting compliance requirements is a top priority for advisors. Using automation to improve the speed and accuracy of transactional processes helps reduce costs and improve accuracy.

Sources:

Cerulli Associates, Federal Reserve, U.S. Census Bureau, Internal Revenue Service, Bureau of Labor Statistics, and the Social Security Administration
A Year of Tremendous Growth for RIAs
https://www.cnbc.com/2019/10/17/these-are-the-changes-and-challenges-keeping-top-advisors-up-at-night.html?__source=sharebar|twitter&par =sharebar
https://www.cnbc.com/2019/10/14/technology-is-redefining-that-client-financial-advisor-relationship.html?__source=sharebar|twitter&par =sharebar
https://www.thestreet.com/personal-finance/financial-planners-see-growing-demand-from-younger-prospects-14772572

Over the past two months, COVID-19 has not only created a global health crisis but also led to socio economic disruption and affected major industry sectors, including healthcare, banking, insurance, capital markets and so on.

Wealth management is one of the vulnerable sectors with highly correlated revenues to capital market performance and has already started experiencing loss in revenue and growth. The stock market response to the COVID-19 pandemic has been panic driven and volatile and could continue to be so until the spread of the virus is contained. With the economic data likely to worsen in the coming months, stock markets could experience another round of correction.

As a result, firms have initially struggled and are now implementing plans to reduce costs, assess spending, with continued efforts to tackle extremely high trade volumes and keep critical processes running. Most firms have now dealt with the initial priorities to ensure large scale business continuity and set up the majority of the workforce to work remotely. These firms are now working to identify data and information security risks and reprioritize organization strategies and projects.

There are a few firms that are yielding benefits of prior investments in digital transformation, automation and infosec who are slightly ahead in the digital maturity curve while others are just starting out to plan and strategize their digital journey for the near future.

From our experience, we believe there are four key themes shaping up during this crisis which will help wealth management firms stay resilient:

Focus on cost reduction and rationalization: To tackle market volatility, there is an increased focus on optimizing costs and improving operational efficiency. With a growing volume of business transactions, deployment of tactical automation solutions to automate trade processing and compliance reporting will embed the much needed flexibility and improve productivity. Outsourcing additional processes for short to medium term will also help address the increase in workload without huge cost investments. On the technology front, leveraging cloud solutions would be a quick win to reduce fixed costs immediately.
Prioritize risk and data security: Given millions of resources are working remotely, companies will have to revisit cybersecurity best practices and enhance/upgrade systems to protect from unauthorized access, phishing scams, etc. With unsecured channels and networks for remote employees, wealth management firms will also need to reassess access to applications depending on criticality due to the increasing threat of cybersecurity. Adoption of multi-factor authentication and enhancing security incident management protocols would be vital in maintaining data security.
Continue to focus on Digital Transformation: Firms need to double down at their digital transformation practice to defend their core business and emerge as a winner in this new normal. Digital analytics is critical for companies to refine their portfolio strategy, help automate critical processes through usage patterns, strengthen market research and insights to better communicate with advisors, broker dealers and investors. The significance of omnichannel and well-designed advisor & investor portals could have never been higher. Simple and intuitive portals will help communicate account/portfolio performance and help stakeholders make data, transaction requests faster and understand how they are being impacted in real time. It’s critical to harness the data across the web, mobile, branches, CRM to make sure the best of the experience can be provided to clients and advisors.
Enhance IT resiliency: Most firms were unprepared for a crisis of this magnitude, given its unprecedented nature. While on the one hand, businesses have managed to get their workforces set up remotely, it is critical that they continue to assess the impact of network traffic, volumes, on the infrastructure. They should also prepare and update plans to address security breaches, network breakdowns, and critical resource unavailability in a proactive manner.

In spite of the downfalls, every crisis helps businesses realize their underlying strengths and helps them define their strategy roadmap for the next journey. We strongly believe that investments in operational efficiencies, digital transformation and customer experience optimization while continuing to work on data security and BCP will be the key pillars of running a resilient business during this crisis. They will continue to remain important in the ‘new normal’ that will emerge post the pandemic as well.

Enabling personalization at scale for consumer banks

Consider the case of a representative customer we’ll call Alex. Alex buys an iphone on his credit card and with this purchase, ends up utilizing around 90% of his credit limit. He gets a sms from his bank within minutes after his purchase, for a credit limit increase offer. With some more expenses expected in coming few days, Alex calls up the customer care and during discussions with call centre rep, is also given an option to convert his purchases into an EMI with attractive interest rate. Alex ends up opting for both Credit limit increase and EMI loan-on-card. What began as a single high-ticket item purchase, ended up becoming a much more engaging experience for Alex.

Welcome to the new world of data science enabled personalization. In the above case, Alex’s bank found that Alex (a credit worthy customer with good credit score) is in a need of extra credit and facilitated the next best action of offering credit limit increase through SMS channel. Not only that, bank’s data science algorithms also defined a price point for EMI loan-on-card to improve Alex’s chances of taking EMI loan and pushed that offer through call centre CRM. Such data-based personalized marketing strategy is the final goalpost for consumer banks to help enable strong customer experience, reduce churn and improve bottom line profitability.

While a very few digital natives and fintech players like Alex’s bank have been able to provide right one-to-one experience to their customer base, rest of the organizations still have a huge opportunity to leverage advanced data science practices and provide personalization on scale for their prospects and existing customers.
Based on our experience of working with some of the consumer banks in US and Latam markets, Incedo has developed a framework called Data Science Maturity Model for Consumer Banking Personalization. The framework describes the maturity levels that currently exist within data science teams for marketing personalization across the ecosystem.

In this post, we describe the Data Science Maturity Model and share the key challenges that are preventing banks from stepping up in their personalization journey to become hyper relevant to their customers.

Stages of Maturity – Data Science Personalization Model for Consumer Banking

Based on our industry experience, it has been seen that banks tend to fall into four main stages of data science based banking personalization maturity

Level 1 : Product Centric

This is ground zero & is an approach used by most of the consumer banking institutions. The goal here is to look at analytics with a siloed product level focus. In context of a banking firm, products may include credit card, personal loan, mortgage etc.

The product heads typically focus on marketing-based strategies leveraging product propensity segmentation or models. The customers who fall in top deciles of each product model end up getting bombarded with offers while there are no contacts with prospects appearing in low deciles of these models. Since there is no focus on customer profitability or life time value, this approach is not optimal from both revenue maximization and customer experience point of view.

Level 2 : Customer X Product Centric

In this stage, firms look at customer management strategies to acquire, cross sell & upsell prospects. The focus is to look at product grid for each customer and identify which product would maximize firm’s profitability, while ensuring good chances of customer to take up the product. Consider an example where a customer has similar probability to take up both Product A and Product B and decision around next best product needs to be taken. In this case, the product which maximizes life time value for client is solicited to the customer.
Based on our experience of enabling customer centric product level recommendations for banks, the move from Level 1 to Level 2 of personalization can lead to incremental bottom-line impact of 10-15%, depending on the existing targeting framework being used at the organization.

Level 3: Customer X Product X Offer Centric

A personal loan offer with an APR of 12% vs APR of 18% would typically have different response propensities & profitability for the bank. For a price sensitive customer with good credit history, response rate would be much higher at 12% offer while bank’s margin & revenue would be higher for 18%. At this stage of personalization, decisioning models (response & value) are built for each Customer X Product X Offer permutation & business simulation & optimization exercises are carried out to identify optimal product & offer for each eligible customer. The final decisioning is based on what PnL KPIs business would want to maximize (e.g. # bookings, $ sales, $revenue etc)
In recent cases where we designed and implemented offer & pricing personalization strategy for our clients, there was an increase of ~10% in terms of revenue of the overall marketing program, in comparison to Level 2.

Level 4: Omnichannel Customer X Product X Offer strategy

The final stage of banking personalization journey involves focus on optimal contact strategy in terms of preferred channel of contact, frequency of contacts etc while also ensuring that right offer is selected for the customer. The data science engine would typically run the simulations based on different data science models to arrive at a personalized strategy for each customer in terms of product, offer & channel contacts, the optimal personalization is then enabled & fulfilled through front end operations teams (call centre, email etc). The right offer through right channel & creative helps improve customer experience & maximize bank’s profitability. While this stage helps maximize the incremental impact of data science initiatives for an organization, it comes with a trade-off in terms of high complexity of implementation.

What’s holding back the consumer banks from moving up the personalization analytics maturity curve?

If the incremental value gained through data science based personalization is so substantial and clear, why is it that not all the banks are already monetizing and achieving impact with it ?
The reason is that most of them continue to struggle with fundamental issues that prevent them from leveraging data science to drive the most optimal & personalized customer experience. These challenges span across data, technology and organizational areas and have been summarized below.

1. Lacking Data Quality & Technology Infrastructure
The first step and a must do in order to create value from data science is accessing all the information that is relevant to a given problem. This entails capturing and generation of data as a first step followed by integration of large stores of data from various sources.

While there are big data platforms and cloud-based services available to store massive amounts of data, the companies are still facing internal barriers in terms of data capture and quality of information. This in addition to long turnaround times to make a switch from legacy technology platforms is acting as a major bottleneck for organizations to build an accurate customer level data repository, which is a precursor to leveraging the state-of-the-art data science tools & algorithms.

2. Insufficient depth in Data Science Capabilities
Personalization is about treating each individual customer as a population of one and designing targeting strategies by leveraging features that encapsulate customer behavior in terms of product usage, spend habits, demographics, interactions across channels, customer journey at a point of time etc. Building such data science based solutions not only requires deep understanding of the sophisticated machine learning & deep learning algorithms but also involves clear understanding of the problem and running a series of business optimizations, before the final recommendations can be implemented in market.

In our experience, we found that most of the consumer banking organizations either don’t have sufficient depth in terms of data science talent and capabilities or have narrow focus on tools and techniques without clear roadmap on pragmatic implementation of data science solutions for driving business impact.

3. Siloed Organizational Structure
The operating model of data science organization for majority of banking institutions comprises of different data science teams operating as islands and tagged to each business unit.
As an example, during Incedo’s data science engagement with one of our client, we found that different data science teams were aligned to each of the product units (credit card, auto loan, personal loan etc) which prevented the firm from designing customer level omnichannel strategy across the product portfolio. The siloed operating model for data science prevents businesses from realizing best possible value from their analytics organization.

Personalized decisioning and targeting of products & offers is a critical imperative for consumer banking firms to operate in the competitive digital environment. To get there, organizations need to identify where they are currently in terms of data science maturity model and should create a roadmap to improve their personalization capabilities.

No matter what maturity stage you are in your data science based personalization journey, our team of experts can help you design and implement data science solutions that create bottom-line impact and provide seamless & wow experience for your prospects and customer base.

Over next few weeks, we would plan to explore and share our perspective in detail, on how to get around the three key challenges in personalization journey. Stay tuned!

Quantum Computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. Quantum computers perform calculations based on the probability of an object’s state before it is measured – instead of just 1s or 0s – which means they have the potential to process more data compared to classical computers.

In a world of constant and extensive technology disruption, with organizations engaged in a battle for survival, the urgency to digitally transform is well understood by almost every large enterprise.

That everyone is trying to go digital is well established. Yet organizations continue to grapple with achieving breakthrough business impact from digital transformation programs.

Recruitment Fraud Alert

What is Contract Pull Through?

Why does pharma need to focus on Contract Pull Through?

Use case deep dive: Portfolio purchasing summary of an account

How to generate the pull through business insights from data?

Unlocking Insights with Incedo’s Data and analytics services on AWS environment

Conclusion:

IncedoPay – Revolutionizing the Accounts Payable Landscape for Banks and Enterprises:

Elevating Payments with Innovation, Security, and Reliability with AWS

Here are three key considerations to keep in mind:

1. The Art of Framing Questions:

2. The Need for Precise Inquiry:

3. Collaboration between Humans and AI:

Incedo Zeal – The design system

Visual Design & Strategy

Content Design & Strategy

Incedo Zeal in Essence

Making digital transformation happen

Digital channels

Automation

Data and analytics

Adoption of standards

Blockchain technology

A Complementary Partnership

Data Mesh

Building Blocks of Data Mesh

Data Mesh Architecture

Data Fabric

Data Fabric Architecture

Principles of Data Fabric

Enhancing Data Management: The Synergy of Data Mesh and Data Fabric

What is Contract Pull Through?

Why does pharma need to focus on Contract Pull Through?

Use case deep dive: Portfolio purchasing summary of an account

How to generate the pull through business insights from data?

Conclusion: CPT – Top of mind for strategic and financial objectives

Enablers of self-service AI platform – Incedo LighthouseTM

Incedo LighthouseTM as self-serve AI at a Pharmaceutical Clinical Research Organization (CRO)

The Value of Personalization for Retail Banking

Building a personalization engagement engine requires integrated capabilities across Data, AI/ML and Digital Experiences

How to turn the personalization opportunity into reality ?

1. Personalized Product & Service Recommendations

2. Personalized Marketing Communication

3. Personalized Digital Experiences

Incedo LighthouseTM – A platform to natively support personalization use cases

Issues with Traditional Segmentation Methods

Growing use of ML/ Data science in Client Segmentation

Client Segmentation across Customer Journey

Segmentation is a growth as well as a Defensive Imperative

Covid-19 and the aftermath: Impact on the industry

What will it take to win in a post Covid world?

Drivers of Digital Transformation

COVID-19 implications and challenges for banks and lending institutions

Four-point action plan and strategy to navigate through the COVID-19 crisis

Expected spike in delinquencies and credit losses post COVID-19

Need for an up-to-date, agile and analytics driven credit decisioning framework:

Incedo’s risk management framework for the post COVID-19 world

Trends impacting advisors and TAMPs:

Strategy to address market trends:

Sources:

Enabling personalization at scale for consumer banks

Stages of Maturity – Data Science Personalization Model for Consumer Banking

What’s holding back the consumer banks from moving up the personalization analytics maturity curve?

Enablers of self-service AI platform – Incedo Lighthouse^TM

Incedo Lighthouse^TM as self-serve AI at a Pharmaceutical Clinical Research Organization (CRO)

Incedo Lighthouse^TM – A platform to natively support personalization use cases