Profitability Pressures
The average global operating profit margin of Wealth Managers fell by 11.6% between 2020 & 2022. Wealth Management (WM) firms have been feeling the pressure of rising cost of operations, with personnel cost estimated to be a major factor. The pre-tax profit margins of WM firms declined by a little more than 12% between 2020 & 2022. This is in addition to revenue pressures due to contraction in Assets Under Management (AUM) due to macro-economic factors, fee income being impacted by increasing shift to passive investments, especially in the US geography, & competition from fintechs.
A technology transformation initiative with strategic objectives focused on leveraging technology to simplify operations, increase efficiency & reduce costs would be needed to address this.
Process Automation
With the goal of operational cost reduction and profitability improvement, tools for focused point-automation could contribute to larger operational transformation. Some examples of point-automation opportunities are:
1) Robotic Process Automation (RPA):
RPA has emerged as a key option for automation of voluminous manual processes like customer onboarding.
Customer onboarding has been a time-consuming and highly manual process in the Banking industry in general, given the large number of documents to be handled and KYC requirements to be complied with. This has gained focus in the wealth management business too, with businesses expanding to cover the affluent segment customers, increasing the number of customers. OCR is the main technology used for data capture from the physical forms and feeding into the systems. This had a handicap of being able to process only structured data and low success rate in processing handwritten data. Customer onboarding typically involves forms hand filled by the customer & investment manager’s front office staff. This also involves handling a lot of unstructured data in terms of different formats, different kinds of documents depending on the client segment, region, etc.
Improvements in OCR cameras and AI capabilities in terms of Natural Language Processing (NLP) & Machine Learning (ML) have now made it possible to automate customer onboarding to a large extent. Business rules can be defined to handle exceptions in terms of missing documents, information etc. This also comes in handy for audit & due diligence requirements during mergers, etc.
Another use case for RPA based automation is the extraction of data from physical financial statements, market reports etc. for financial analysis, which again has the potential to reduce manual effort & nimprove productivity.
2) Open API Architecture:
Investment managers often have access to a lot of ‘client permissible’ financial data from client systems, like portfolio performance & holding details with other investment managers, gains or losses from other investments or other sources of income, etc . to fine tune investment strategies, tax loss harvesting, etc. WM firms also integrate with external stakeholder systems like embedded service providers.
Adoption of an Open API based architecture helps automate these interfaces and automated processing of data & feeding to transaction systems.
Cognitive Technologies For Improving Productivity:
Predictive AI brings tremendous capability for deep personalization in identifying individual client preferences and tailoring the offerings.
Generative AI is increasingly being used for automating the administrative functions of financial planning – customer communication, market and investment analysis, etc. – for the advisor, helping free up time for improving quality of advice. A notable example of recent initiatives in this space is Tifin.AI, launched jointly by fintech Tifin & J P Morgan. The venture is an innovation platform that will enable fintechs to develop AI powered WM solutions for portfolio insights to advisors, enabling alternative investments, and employee benefits wealth management, based on Tifin.AI’s conversational AI framework.
Some examples AI enabled automation are:
1) Advisor Marketing & Communication:
Solutions based on generative AI software like Chat GPT help advisors deliver effective & highly personalized communications to their clients & prospects, relating to their portfolio and / or financial education. Some examples are:
2) Investment Assistance:
AI powered tools for market analysis & forecasting. Some of the solutions that have emerged in recent times in this space are:
3) AI Powered Robo Advisors:
Robo Advisors traditionally use algorithms to arrive at investment decisions based on a set of parameters like financial goals, risk tolerance etc., to select the right asset. AI enabled Robo Advisors use Machine Learning (ML) to create the best fit for the customer and automate use of customer financial data for tax loss harvesting, an enabler for offering Personal Indexing at scale.
With continuing evolution of digital & cognitive technologies, organisations need to continuously look out for opportunities to leverage them for improving operational efficiency.
Co-authored by Eswaran Swaminathan, and Venkatesh Padmanabhachari
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