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:

  1. 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.
  2. 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.
  3. 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.
  4. 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:

  1. 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.
  2. 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.
  3. 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.

Traditionally Wealth management technology for wealth managers was more focused on Advisor technology. As the business model evolved from Product Centric to Relationship Centric and the client expectations changed, the wealth management firms started to focus on investor technology to create a competitive differentiator. As per numerous studies, the wealth management client experience lags behind other industries and its financial services peers such as Cards, Banks, and even Insurance.

Clients focus a lot on the digital experience provided by the firms to decide on their future financial advisors. As a result, the client portal is a critical investment that firms have been making in this space. Per a study, 93% of the studied wealth managers have either implemented a client portal or are in the process of implementing it. Client portals can become a hub where clients access all their financial information. They are a valuable tool to retain clients and grow your practice by increasing assets with existing clients and attracting new prospects. When advisors and wealth manager firms find it difficult to differentiate themselves from other competitors, the client portal can become that key differentiator and a source of competitive advantage.

In this whitepaper, Gaurav Sehgal, Vice President, Financial Services, Incedo talks about the critical importance of client portals and digital experience for Wealth Management organizations.

Business executives today are increasingly required to make decisions in a dynamic environment under the following circumstances:

  • Uncertainty: The situation evolves with time and it is difficult to define business rules a priority. There is uncertainty in factors impacting business.
  • Contextual: Increasingly, external factors have a significant impact on how decision makers need to respond to situations. So, only a certain set of internal factors cannot be taken into account for decision making.
  • Iterative: Decisions are no longer limited to linear cause-and-effect scenarios. Instead, decisions have to be made in an iterative manner in a ‘test and learn’ mindset.

There is clearly an opportunity to provide decision automation solutions that can leverage AI/ML technologies to augment the human decision-making process. We believe that data and AI/ML can be leveraged to develop the next generation of decision automation solutions.

About the Author

vivek-kakde
Vivek Vijay Kakade

Senior Director of Product Management, Incedo Inc.

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 :

  1. 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.
  2. 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.
  3. 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.

value-potential-personalization

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.

  1. 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.
  2. 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.
  3. 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 LighthouseTM 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 LighthouseTM 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.

incedo-personalization-solution

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 LighthouseTM – A platform to natively support personalization use cases

With our proprietary platform Incedo LighthouseTM, 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 LighthouseTM helps in protecting against these issues in a very direct manner – more on that in the next blog!

The client, a mid-tier US bank, was using manual model monitoring processes for their marketing and risk management departments. The regulators, in external audit findings, pointed out errors in the model monitoring outcomes and it was mainly because of manual processes in place. The bank had an inventory of 20+ models across credit risk, market risk, stress testing etc., which were monitored by sub-teams. There was a clear opportunity for standardization and centralization through a single source model monitoring platform.

Lighthouse enabled model risk management solution

The Lighthouse enabled ‘System of Insights’ for the existing model inventory acted as a one-stop solution for the risk management team. It enabled executive level views and recommendations and report generation was made available at the click of a button. Model-level, deep dive validation analysis was made possible with Diagnostics like KS, Lift Curve and PSI etc. This AI/ML enabled tool acted as a workflow enabler and one-stop shop for all the regulatory model risk assessments. The impact were as following:

80%-efficiency-and-productivity

80% Efficiency and productivity gain through automated model monitoring

regulatory-compliance-through-resolution-audit-reported

Regulatory compliance through resolution of audit reported findings for model risk management

centralization-standardization-model-monitoring-across-bank-processes

Centralization & Standardization of model monitoring across the bank’s processes

The client, a top 10 bank in the US, saw depleting performance in terms of the accuracy of existing risk segments. In the last few years, it observed an increase in the operational cost of its collections operations. The challenges were as following:

Sub-optimal contact and offer strategy: With high number of contacts per customer, the cost of service was escalating. The bank was not effective in when to pitch what offers to delinquent customers. It was wasting resources and a number of man-hours in the sub-optimal process.

Deteriorating performance of self cure and roll rate segment strategy: The bank’s delinquent customers and its self cure / roll rate percentages were headed southwards. Essentially, more customers were rolling from the 60-day overdue period to the 90-day one, thereby sharply affecting the roll rate percentages.

Incedo LighthouseTM enabled the bank to run a leaner, much more efficient in-house collections operations. It also drastically reduced credit losses and dollar write-offs. Some of the prominent benefits were as following:

Decrease in dollar write-offs for cards and personal loan portfolios
0 %
Decrease in channel costs through optimal contact strategy
0 %

The client, a banking and financial services company, tripled digital sales and increased conversion of its credit products. It had the vision of digitally transforming its business operations. With a clear action plan for digital-enablement, the goal was to deliver personalized experiences to its customers. The bank had the following key objectives: drive digital sales with AI/ML enabled cross-sell personalization, improve conversion rates for its credit products and increase loan disbursal through a new targeting framework.

Incedo leveraged the Full stack Data, AI and Cloud-enabled Lighthouse platform for AI/ML driven cross-sell personalization. It benefited the client with:

Shift in digital sales in a 6-month period
0 x
Increase in dollar loans disbursed through the new targeting framework
0 %
Sales improvement in conversion rates for the 3 credit products
0 x

About the Customer

BSI Financial an industry leader providing mortgage solutions to consumers. In addition, it provides Digital platforms to independent mortgage servicing companies to drive operational excellence and intelligent customer experience. BSI had decided to pivot the enterprise to “Cloud-first” and “Ai-first” strategy, focus resources on customer engagement and innovation, and enable greater agility.

Platforms modernization and operations transformation were identified as a priority to create capacity for growth, lower operating costs, and allow for scalability. This encompassed loan servicing, MSR portfolio due diligence, Internal and External reporting, and Mortgage Operations.

What Incedo Did

BSI chose Incedo as a partner to re-imagine and engineer cloud native SaaS Platforms on AWS cloud. This encompassed:

  • Architecture Assessment and Modernization
  • Reimagined Customer Journeys
  • Intuitive user experience to encourage higher self-service
  • Delivered on micro services architecture and APIs for scalability
  • Continuous optimization

Impact

promotion-brand-messaging

Promotion of brand and messaging for clients to end customers was simplified

enabling-pay-subscription-model

Enabling pay per use subscription model for customers

seamless-integration-offerings

Seamless integration of third party offerings and services

monetization-internal-IP

Monetization of internal IP and capabilities created over time

increased-self-service-platform

Increased self-service on the platform and reduced cost to serve for end customers

enhanced-customer-experience

Enhanced customer experience from simpler onboarding and engagement for end customers

increased-upsell-opportunities

Increased cross and upsell opportunities for end customers

The client, a mid-market specialized mortgage lender with their business portfolio comprising origination and servicing for MSRs was looking for a strategic partner in their growth journey, to uplift customer experience, and digitally transform the overall loan servicing operations. The strategic priorities for the client were – Significantly increase digital self-service, improving customer NPS, and improve mortgage operations productivity.

Incedo’s modern and intelligent SaaS solutions based on cloud, automated workflows, and AI-driven personalization have helped the client achieve improved efficiency, agility, and scaling while being able to respond to new opportunities and unanticipated situations, such as the COVID-19 pandemic. Incedo’s solution benefitted the client with:

70-90% straight through processing

23% increase in self-service

35-45% cost optimization

Prior to the COVID-19 pandemic, the Wealth Management industry was experiencing unprecedented change and disruption with – changing customer and advisor expectations, revenue and business model disruption and unyielding pressure on costs. Going into the pandemic leveraging new and emerging technologies was a primary driver for change in the industry. What have we learned these past six months and how is it altering plans for the future. Is leveraging technology still the way forward for the Wealth Managers navigating into a VUCA world?

How can you build disruption immunity with Digital Transformation for your wealth management business and make it Pandemic proof?

Get powerful insights from Top Global Wealth Management CXO’s on the disruptions in the wealth management ecosystem and the emerging trends. The discussion includes:

  • Emerging Trends in Wealth Management Industry in the backdrop of Pandemic
  • Change in Client’s expectations and its impact on Advisor Engagement and Client Experience
  • Disruptions in the Wealth Management Ecosystem (New Players)
  • Change in the Operating Models with Technology

About the speaker

nitin-seth
Nitin Seth

CEO, Incedo

Nitin Seth is an accomplished industry leader with a unique combination of experiences – of an entrepreneur, a management consultant and a senior executive. Prior to Incedo, Nitin was the Chief Operating Officer of Flipkart, where he was responsible for customer and supply chain operations, strategy to execution capabilities and corporate functions for the company. He has also headed Fidelity International as the M.D and Country Head for India. Prior to Fidelity, Nitin was the Director of McKinsey’s Global Knowledge Centre in India (McKC). He has been elected twice to the NASSCOM Executive Council and was also the Chairperson of the Nasscom Regional Council (NRC) in Haryana. He is an avid speaker in the areas of entrepreneurship, business transformation and technology.

Muk-mehta
Mukesh (Muk) Mehta

Chief Information Officer, Assetmark

Mukesh (Muk) is responsible for Assetmark’s technology strategy and implementation with a specific focus on creating systems and solutions that help advisors achieve success. Additionally, he identifies and incorporate systems enhancements to improve AssetMark’s service and operations capabilities that support advisors. Previously, Muk was the Chief Information and Technology Officer at Cetera Financial Group, the second-largest network of broker-dealers serving over 9,500 independent advisors. Prior to that, he was a Managing Director and Corporate CIO at TD Ameritrade and was also SVP and CIO of Schwab Institutional. Muk has been featured in Barron’s, InvestmentNews, RIABiz, and Marketwired. Muk received a Bachelor of Arts degree in Mathematics and Economics from Rutgers University and is a graduate of the Stanford Graduate School of Business Executive Program. He holds series 7, 24 and 63 licenses.

mike-ragunas
Mike Ragunas

Chief Information Officer, Cetera Financial Group

Mike brings extensive experience delivering high-volume critical systems including online commerce, SaaS/API-based middleware, contact center, fraud and claims platforms, store/branch technology, and data center infrastructure. Mike most recently served as chief technology officer for Payfone, Inc., a leading provider of mobile network-based authentication and fraud avoidance solutions—protecting mobile data as well as call center interactions for large financial institutions and other clients needing mobile security solutions. Prior to joining Payfone, Mike served as chief information officer for Fanatics, Inc., a leading online retailer of officially licensed sports merchandise.

He has earned numerous professional recognitions throughout his career, including being named to InfoWorld Magazine’s “Top 25 Most Influential CTOs” as well as Computerworld’s “Premier 100 IT Leaders”. Mike has also been awarded multiple patents for technology and business process innovations.

Jason-Roberts
Jason Roberts

Chief Technology Officer, Cambridge Associates

Jason is the Chief Technology Officer at CA and is responsible for leading the information technology organization and functions. Jason has more than 20 years of industry experience and has been with the firm since 2014. Prior to joining Cambridge Associates, Jason spent 15 years at Sapient Corporation. Most recently, he led Sapient’s Global Capital Markets practice where he was responsible for setting strategy and leading critical engagements with clients. Prior to taking on the practice leadership role, Jason was responsible for several key relationships with leading asset managers and hedge funds. His background includes leading IT and business consulting initiatives across multiple functions within the investment management process. Prior to joining Sapient, Jason worked for PriceWaterhouseCoopers and Accenture.

With accentuated pressures due to the Covid pandemic on revenues and calls for cost optimization & prudence on middle-back office functions, the wealth management firms are facing multiple external and internal challenges. However, certain firms are winning post-Covid leveraging Next Generation Operations Transformation.

Watch this webinar to understand how Next Generation Transformation can help you drive maturity and bring efficiencies to manage challenges for your firm in the current times.

Agenda

  • What are the key forces impacting the industry especially wealth management operations: We will be covering the key trends in the wealth management industry including fintech disruption, cost pressures, regulatory requirements, emphasis on advisor & client experience, etc.
  • How did COVID accentuate the issues wealth management firms were facing: We will cover how COVID-19 accentuated the ongoing issues and trends in the wealth management industry and how firms have traditionally dealt with these issues
  • Approach for wealth managers to begin their next generation operations strategy: We will go over the key objectives of wealth managers and outcomes they are looking to achieve (e.g. tackling seasonal market volatility, real-time compliance alerts, etc.) and go over the approach for next generation operations transformationApproach for wealth managers to begin their next generation operations strategy: We will go over the key objectives of wealth managers and outcomes they are looking to achieve (e.g. tackling seasonal market volatility, real-time compliance alerts, etc.) and go over the approach for next generation operations transformation
  • Use case: We will review a use case covering the current implementation and the difference made by using next generation operations transformation

About the speaker

pamela
Pamela Sharma

Senior Vice President, Business Operations

With more than 25 years of robust experience in service operations, dedicated project-based team organizations, induction of newer technologies along with required certifications and data security, Pamela is well adept in designing and spearheading digital transformations in diverse process areas or business units for the clients. She follows an innovative, analytical, adaptive and integrative approach to develop and deliver consistently improving outcomes for the clients that reflect in their capabilities and performance. She believes in clients to be partners for growth irrespective of their size and scope. Pamela heads the business operations and automation business at Incedo.

Swapnagandha-Gite
Swapnagandha Bhausaheb Git

Associate Director, Financial Services

Experienced Financial Services leader with a demonstrated history of working in the IT Strategy & Operations space, she is an IIM Calcutta MBA graduate and helps Incedo’s Financial services clients with their Data , Analytics and Business Intelligence problems.

Covid-19 has introduced a new set of challenges to the Wealth Management industry. Already it was battling with advisor attrition, changing client preferences, disruption due to fintech, lack of scalability, and mounting cost pressure due to no commission fee. COVID-19 has accentuated this need for wealth management firms to address this challenge and have a personalized approach to acquiring and retaining the right set of Advisors and enabling them to service their clients better. AI-powered insights and experiences can be a great enabler.

Watch our webinar to hear from our experts on how Incedo’s AI-Enabled Advisor personalization framework can enable the wealth management firms to segment the advisors better and drive better results, curating contextual & personalized experiences through the advisor Journey.

This webinar covers how Wealth Management firms can optimize the Advisor Engagement using AI & Analytics

  • Understanding the key forces shaping the Wealth Management landscape in the backdrop of COVID-19
  • The Root cause analysis of Exodus of Wealth Management Advisors
  • Understanding how wealth Management firms are leveraging Artificial Intelligence for curating personalized experiences throughout the advisor Journey

About the speaker

gaurav-sehgal
Gaurav Sehgal

Vice President @Incedo Inc.

Gaurav is a leader in Incedo’s wealth management practice . He has been with Incedo for the last 9 years and has led multiple engagements for our wealth management clients focusing on technology solutions for Streamlining Advisor and Client experience, Digitizing Operations, Data & Analytics.

Afrozy-Ara-Sr Director-Strategy & Analytics
Afrozy Ara

Director, Data Science & Analytics @Incedo Inc.

Afrozy is a Data Science leader with Global experience in partnering with Fortune 500 clients to deliver significant KPI movements leveraging Analytics & AI. She brings in multi domain experience with expertise with Wealth Management, Life Sciences and Telecom Clients She lives in the San Francisco Bay area and enjoys hiking and writing, along with her passion for all things Data.

The COVID-19 pandemic has led to a breakdown of Risk models across banks. The global economic crisis of this magnitude has led to a sudden shift in the credit profile of the customers. It has also unearthed the flaws in the underlying risk assumptions by banks based on a stable state.

To cope up with this volatile scenario banks needs not only advanced AI and ML algorithms-based models but support agile services which can take care of high-frequency data. Our risk offering combines our proprietary AI and ML based platform, with a agile risk mitigation framework to maximize risk-adjusted returns for banks.

In this webinar we have covered different dimensions of our data science and analytics-driven approach for accelerated credit decisioning, which includes:

  • Responding to a sudden shift in the credit profile of customers through effective early monitoring triggers
  • COVID command center for banks by rapid identification and quantification of bank’s risk exposure
  • Develop roadmap and strategies to mitigate the short, medium- and long-term impact of COVID-19

About the speaker

diveysharma
Divey Sharma

Director, Analytics and Data Science at Incedo Inc.

Divey is seasoned analytics professional with strong experience in leading teams, responsible for designing impactful data driven solutions for banking domain across multiple geographies, businesses and functions.

Over the last few years, the independent broker dealer industry in USA has seen a lot of churn. Many large broker dealers grew their book of business and registered representative base inorganically, by taking over their smaller counterparts. What came as legacy was a host of disparate systems and operational teams, all doing similar tasks.

The broker dealers were still grappling with the problem of integrating the systems of their new found entities, when the Department of Labor (DOL) threw them another curveball in April 2015. This curveball has since come to be called as DOL fiduciary rule and threatens to alter the entire dynamics of the industry, which has operated on a variable compensation model for decades. It will force the industry, which has long regarded its role as transaction facilitators, to wear the hat of the advisors and discharge all the onerous responsibilities that come with it. But what exactly is the DOL fiduciary rule and what will be its impact? This white paper is an attempt to demystify the ruling and aims to propose what broker dealers should be doing to prepare for this seismic event.

When the GDPR became a law in Europe, American companies not doing business in Europe heaved a sigh of relief that they did not have to comply with the stringent requirements laid out in the law. However, as Arab spring showed us, movements have a way of spreading beyond borders. Privacy spring has now come to USA in the form of CCPA (California Consumer Privacy Act). CCPA provides protection to all California residents and lays down markers on how the consumer data can be used by various businesses. THE ACT PROVIDES THE CALIFORNIA RESIDENTS THE RIGHT to know what kind of data is being collected for them and for what purpose it is getting used.

  • To request deletion of the personal data
  • To opt out in case their personal information is getting sold (defined broadly to include any valuable consideration) to third parties.

In this whitepaper, Mr. Gaurav Sehgal (VP, Financial Services, Incedo) explains the impact of CCPA on wealth management industry and more narrowly on the impact of legislation on wealth managers. Learn about how wealth manager should approach the legislation and be prepared for its implementation.

As the race for top financial advisor talent heats up, Broker dealers are increasingly setting aside higher recruiting budgets to attract top financial advisors from their competitors. Any given month, AUM north of 500 mn dollars is moving from one broker dealer to another. The financial advisor’s first interaction with the prospecting firm is via the advisor onboarding process.

A good workflow driven Online onboarding process can deliver a transformational experience to the advisors and the home office teams can drastically cut down on the affiliation cycle times. Leveraging from what has worked for our clients (broker dealers, banks, TAMPs) in implementing workflow driven processes, the whitepaper distils the key tenets of a good onboarding system from various engagements.

Download this whitepaper to get insights into:

  • E-onboarding
  • Benefits of online onboarding
  • Ensuring seamless workflow driven onboarding process

Investor protection framework has been a key focus area for regulators for a long time. While the Securities and Exchange Commission’s (SEC) fiduciary rule governs the financial advisors, the brokers have been regulated by the Financial Industry Regulatory Authority (FINRA) Rule 2111 (Suitability). The Department of Labor (DOL) fiduciary rule tried to create a common framework for the governance for brokers and advisors, but the rule was revoked following stiff opposition from brokers and industry groups. In June 2019, the SEC passed the Regulation Best Interest (REG BI), which establishes that broker-dealers and financial advisors need to work in the best interest of the consumer and to eliminate conditions that further a firm’s interest over a client’s interest.

This white paper will try to assess how the regulations will impact the industry, brokers, and the various systems and applications.

An Independent broker dealer with the second largest broker dealer network in the US, lacked executive view across the business network with the distributed data that was making it impossible to view key metrics, gain insights, and simulate “what-if” scenarios. They wanted to improve the sub-optimal mobile features in the existing BI platform while dealing with the increasing cost of 3rd party tools, high time to market for reporting requests, and poor user experience with reports.

Incedo’s blend of data warehousing, reporting, visualization, and analytics solution resulted in the following:

360-degree-view-firm-for-executives

360 degree view of firm for executives

flexible-access-to-data

Flexible access to data and self-service at enterprise level

light-fast-dashboards

Light and fast dashboards

reporting-self-service

Reporting self-service leading to reduced ad-hoc requests to BI team

reduced-cost-of-ownership

Reduced cost of ownership with scaling of data

reduction-in-overall-licensing

Reduction in overall Licensing and AMC cost with new licensing model

assisted-executive-management

Assisted executive management to make smarter business decisions through comprehensive sets of reporting and insights

A banking and financial services conglomerate having operations in US and Latin America has a strategic vision of transforming into a “Digitally-enabled” bank to deliver a highly engaging omnichannel customer experience. They were facing several challenges in accelerating the pace of digital transformation, which made them perform sub-par on the digital metrics.

Incedo teamed up with the client, who has products and services including personal banking, corporate banking, domestic lending, and foreign exchange. On facing disruptions, the client wanted to plug the gap by a three-pronged approach.

  • Improve Digital engagement and enhance customer experience
  • Increase sales leveraging digital channels
  • Launch new age products using open banking platform and tying up
Incedo’s digital consulting team collaborated with the bank’s retail business, digital and technology leadership teams to clearly define the problem statements aligned to their business KPI’s to translate them into a defined digital transformation approach and priorities. Incedo adopted a multidisciplinary approach combining platform with services along with a three step approach for digital transformation to impact:

2X improvement in conversion rates across channels

improvement-digital-channel

30-50% improvement in digital channel contribution for sales across products

109% improvement in credit disbursement driven by digital targeting

improvement-in-interest-revenue

135% improvement in interest revenue from existing loans driven by ML driven personalized pricing

Driving innovation and onboarding new use cases, while reducing cost and TAT leveraging Digital platform

Inculcating Advanced ML and Cloud capability within Bank’s marketing and IT teams, to drive data science use at the bank

A US-based mortgage solution provider and a full-service lender servicing customers for more than three decades wanted a scalable solution that optimizes agility with minimal costs. They wanted a self-serving loan solution based on the changing load requirements of concurrent users.

So when our client wanted a self-serving loan solution for its end customers, while reducing customer service overheads, Incedo rose to challenge to help them build a platform that reduces the frequent downtime, eliminating the capacity and load related issues, while radically reducing the processing time.

With new solutions and a platform in place, the client generated results through:

Cost saving of $1 million, spread over a three-year period, in license fees.

agility-to-scale-and-seamlessly

Agility to scale and seamlessly manage and optimize underutilized services as well as demand spikes, reducing infrastructure costs with timely reviews and reporting

Self-serving portal for customers to have a better control over their account thereby reducing manual intervention of customer care team and reduced process time, leading to an improvement in customer acquisition, retention, and lower customer service overheads.

Intuitive and superior UX for a superior customer experience.

100% availability of loan portal, high performance with low latency and response time.

A financial services company specializing in loan and asset management services, licensed in 50 states in the US was looking for better control over increased cost and performance issues arising because of maintaining the capex and increasing demand of scalability of the applications. The operations needed to be scalable and agile and there was a need to cut down on processing time. Moreover, the client was looking to provide a responsive, high-touch customer experience to its end customers. This is where Incedo engaged with the client to provide a consulting assessment, develop and implement a customized AWS migration strategy.

With a detailed requirement analysis keeping in mind that the client’s key priorities, Incedo build solutions that helped the client its modernization needs while evaluating multiple options that generated significant impact like:

 
improved-delivery-effectiveness-through-higher-productivity

Improved reliability and availability of client product and services with a seamless transition to business as usual with minimum downtime during migration with successful go-live

significantly-reduced-lead-time

Reduction in turnaround time and cost

improved-security-manageability

Improved security and manageability

improved-operations-efficiency

Greater efficiencies with increase in speed for database refreshes to update development data with current data pulled from production

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

  1. 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.
  2. 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.
  3. 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.
  4. 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.

factors-driving-increase-datascience-machine-learning

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 TypeBased onSegment ExampleData Required
Geographic & DemographicLocation, Age, Income, Profession, genderUrban vs Rural
Millennials vs Baby Boomers		Client & Account Data
Value/ Potential ValueGDC , AUM, Type of Revenue (Fee vs Commissions), NetworthUNHW, HNW, Mass Affluent, Masses
High Value Vs Low Value		Trades Date, Advisory Billing data, Positions Data
Risk ProfileGoals, Risk Profile, Return objectives, Time HorizonConservative vs Aggressive InvestorSuitability Data
BehavioralTrading Frequency,& PatternsPassive vs Active InvestorTrades Data, Positions Data, CRM
TechnographicEngagement with ApplicationsTechnologically challenged vs Tech Savvy ClientsPortal & 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

wealth-management-framework

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.

Next generation operations transformation

  • 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:

  1. Automation solutions deployed in silos instead of reviewing the overall customer journey
  2. Firms are automating the current underlying processes as-is which could be inefficient and hence not achieving higher returns on investment
  3. Focus on automating the product features rather than customer journey
  4. 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.

advisor-movement-study

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

expected-wealth-transfer

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.

key-goals-or-business-metrics

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:

Drivers of Digital Transformation

  1. 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.
  2. 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.
  3. 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.
  4. 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

  1. 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.

    Covid-situational-risk-assessment

  2. 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.

    early-warning-alerts-heuristic-risk-scores

  3. 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?executive-command-centre-for-COVID-risk-monitoring
  4. 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.
    personalized-credit-interventions-strategy

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.
expected spike in delinquencies and credit losses post covid-19

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.

Roadmap-for-post-Covid-credit-risk-management

  1. 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.

COVID-risk-assessment-and-early-monitoring-systems

  1. 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.

credit-risk-tightening-measures

  1. 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.

  1. 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.

  1. 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.

  1. 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:

  1. 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]
  2. “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.
  3. 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.
  4. 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
  5. ‘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:

  1. 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
  2. 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.
  3. 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:

  1. Cerulli Associates, Federal Reserve, U.S. Census Bureau, Internal Revenue Service, Bureau of Labor Statistics, and the Social Security Administration
  2. A Year of Tremendous Growth for RIAs
  3. 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
  4. https://www.cnbc.com/2019/10/14/technology-is-redefining-that-client-financial-advisor-relationship.html?__source=sharebar|twitter&par=sharebar
  5. 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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

Stages of Maturity – Data Science Personalization Model for Consumer Banking

Implementation Complexity

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.

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