Financial Services - Incedo Inc.

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Labor (DOL) fiduciary rule tried to create a common framework for the governance for brokers and advisors, but the rule was revoked following stiff opposition from brokers and industry groups.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:

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.

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:

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:

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:

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

Changing client mix and expectations,
Fee compression
Fintech disruption.

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

Issues with Traditional Segmentation Methods

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

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

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

Growing use of ML/ Data science in Client Segmentation

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

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

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

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

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

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

Client Segmentation across Customer Journey

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

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

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

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

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

Growing popularity of ESG with the younger investors or the increased sales of life insurance to the urban middle age group during pandemic are good examples of how advisors and product companies align product recommendations with client segment’s preferences.
Similarly, if the clients are more focused 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 focussed above on how client segmentation can turbocharge an advisory practice’s growth, it is also a defensive imperative for the wealth management industry. FAANGs have perfected customer segmentation and personalization to an art form and are eagerly eyeing trillions of dollars of the wealth management industry. Wealth managers would do well to weaponize their data by using the power of machines and insulate themselves against the looming threat of big pocketed disruptors.

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

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

Covid-19 and the aftermath: Impact on the industry

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Prospecting & Acquisition

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

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

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

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

Growth & Expansion

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

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

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

Maturity and Retention

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

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

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

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

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

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

Drivers of Digital Transformation

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

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

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

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

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

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

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

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