Profitability pressures
The average global operating profit margin of wealth management fell by 11.6% between 2020 and 2022. Wealth Management (WM) firms have been feeling the pressure of rising costs of operations, with personnel costs estimated to be a major factor. The pre-tax profit margins of WM firms declined by a little more than 12% between 2020 and 2022. This is in addition to revenue pressures due to contraction in Assets Under Management (AUM) due to macroeconomic factors, fee income being impacted by increasing shifts to passive investments, especially in the US geography, and competition from FinTech.

A technology transformation initiative with strategic objectives focused on leveraging technology to simplify operations, increase efficiency and 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 the 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 of 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 a low success rate in processing handwritten data. Customer onboarding typically involves forms hand-filled by the customer and investment manager’s front office staff. This also involves handling a lot of unstructured data in terms of different formats and 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) and machine Learning (ML) have now made it possible to automate customer onboarding to a large extent. Business rules can be defined to handle exceptions, such as missing documents or information. This also comes in handy for audit and 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 and improve productivity.

2. Open API architecture:
   Investment managers often have access to a lot of ‘client permissible’ financial data from client systems, like portfolio performance and 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 and feeding to transaction systems.

Cognitive technologies for improving productivity:
Predictive AI brings tremendous capability for deep personalisation 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 the quality of advice. A notable example of recent initiatives in this space is Tifin.AI, launched jointly by FinTech Tifin and J P Morgan. The venture is an innovation platform that will enable FinTech 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 of AI-enabled automation are:

1. Advisor marketing and communication:
   Solutions based on Generative AI software like Chat GPT help advisors deliver effective and highly personalised communications to their clients and prospects relating to their portfolio and/or financial education. Some examples are:
   

2. Investment assistance:
   AI-powered tools for market analysis and 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 the use of customer financial data for tax loss harvesting, an enabler for offering Personal Indexing at scale.

With the continuing evolution of digital and cognitive technologies, organisations need to continuously look for opportunities to leverage them to improve operational efficiency.

Co-authored by Eswaran Swaminathan, and Venkatesh Padmanabhachari

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