How wealth managers can use insights from AI and machine learning

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As more and more wealth managers begin to appreciate that insights extracted from big data can be a significant competitive differentiator, applications powered by AI, cognitive computing, and machine learning are making industry inroads.

These emerging-technology applications simulate human thought processes through self-learning algorithms that use data mining, pattern recognition, and natural language processing to mimic the way humans think.

Analytics applications are no longer merely the purview of marketing and customer segmentation, but are now being used to tap into streams of structured and unstructured data to capture fact-based behavioral information. For example, firms are implementing customer-centric, decision-making frameworks to determine the next best action (NBA) for their clients. Driven by models that capture customers’ life-event patterns, investment behavior, risk tolerance, and other activities, NBA engines are becoming central to the digital transformation journeys of wealth management firms.

Data explosion

Exponential increases in computing power and data storage capabilities have led to the rise of AI and machine-learning systems. In fact, data is growing faster than ever before, and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.[1] This explosion of customer data makes it easier for firms to leverage the next-best-action framework to design personalized products and service delivery models (as “one-size-doesn’t-fit-all”). The rise in passive investing by clients and the move toward lower fees have also led wealth managers to invest heavily in the technology to reduce operating costs and comply with regulatory mandates.

Machine learning will help in processing large amount of unstructured data and decipher it into habitual patterns and meaningful insights. These insights will support in predicting client’s behaviors and events even before the actual occurrence: “Know before they know.

AI early adoption

The wealth management industry is an early adopter of artificial intelligence and is being used by wealth management firms to transform client experience aspects; from front-end to back-end. Automated advisors utilizing AI are expected to control assets worth US$2.2 trillion by 2020.[2] Moreover, cognitive applications are helping wealth firms deliver deep personalization, and answer complex client questions in real time through expert virtual advisors that act as a conversational interface with clients. In fact, Wells Fargo piloted an AI-driven solution on Facebook Messenger as a channel to personally address customer queries.[3] In fact, almost 62% of the systematic managers use machine learning to enhance the investment returns as per a survey conducted by Barclays.[4]

Investment managers are also using predictive analytics to generate investment ideas and to predict assets at risk. AI-enhanced data analytics can complement traditional financial analysis by offering unique insights. For instance, BofA Merrill Lynch is experimenting with an AI stock-picking tool to help identify value in small-cap stocks that conventional analysts might miss.[5] Another example is of JP Morgan which recently won an award for its analytics platform, SPARTA, which includes real-time calculation of performance, contribution, and attribution, in addition to on-the-fly grouping and advanced ex-post risk analytics.[6]

Key Application Areas of AI and ML in Wealth Management Firms

Source: Capgemini Financial Services Analysis, 2017

What’s ahead?

As technology advances become vital differentiators, competition between non-traditional and traditional firms is bound to heat up. Strategic executives will find ways to distinguish their firms through better service and operational efficiency with AI, ML, and cognitive analytics applications across all wealth management stages.

AI will provide applications towards providing personalized solutions as it learns from client’s personal data (shopping, web browsing, social media, hobbies, travel, application usage, etc.) and financial data (portfolio allocations, account statements, fees, financial planning, interactions, etc.).

Predictive analysis will use the huge pool of data related to investments, sentiments, and emotions to anticipate the future state by applying machine learning and artificial intelligence. A key example to note is of Chase Manhattan, which deployed predictive analysis across the retail financial consumer data and identified the customers who are most likely to refinance their mortgage. This helped them to propose a better proposition to the client with attractive interest rates. They were also able to identify the group of customers not suitable for the mortgage product offerings from the bank.[7]

Another likely scenario lies with services to the lower end of the wealth market which may morph into sophisticated automated propositions in future. Machine learning and artificial intelligence can help predict the future HNWIs (high- net-worth individuals) among the small retail clients as well as provide personalized solutions to individual prospects as per their goals by helping them engage in a planned and strategic manner.

Another path breaking technology, quantum computing, has the potential to bring a fundamental shift in how the financial services industry operates. The data encryptions technique used currently can become obsolete soon owing to the power of quantum computing which can help in reducing the database request times, financial forecasting, and risk analysis. In fact in stock markets, quantum computing can help in finding an effective frontier of portfolios with best possible returns, depending upon the risk level.

Overall, automated advisory and other AI applications could appreciably increase wealth management efficiency and business opportunities in the years ahead. Find out more in Top-10 Trends in Wealth Management 2018, a report from Capgemini Financial Services.

[1] Analytics Week, “Big Data Facts,” Vishal Kumar, March 26, 2017, https://analyticsweek.com/content/big-data-facts/, accessed July 2018.

[2] Medici, “By 2025, AI Will Have a 5-Trillion-Dollar Direct Impact on the Workforce”, May 5, 2016, https://letstalkpayments.com/by-2015-ai-will-have-a-5-trillion-dollar-direct-impact-on-the-workforce/, accessed July 2018.

[3] Wells Fargo, “Wells Fargo Testing Bot for Messenger Featuring New Customer Service Experiences,” April 18, 2017, https://newsroom.wf.com/press-release/community-banking-and-small-business/wells-fargo-testing-bot-messenger-featuring-new, accessed July 2018.

[4] Rise of the Machines, June 15, 2017, https://www.investmentbank.barclays.com/our-insights/rise-of-the-machines.html, accessed July 2018.

[5] CNBC, “Machine learning is transforming investment strategies for asset managers,” June 6, 2017, https://www.cnbc.com/2017/06/06/machine-learning-transforms-investment-strategies-for-asset-managers.html, accessed July 2018.

[6] Waters Technology, “Best Analytics Initiative: Buy Side – JPMorgan Asset Management (Sparta),” January 16, 2017, https://www.waterstechnology.com/awards-rankings/2480253/aftas-2016-best-analytics-initiative-buy-side-jpmorgan-asset-management-sparta, accessed July 2018.

[7] “Big Data and Investment Management”, May 2015, https://www.bnymellon.com/_global-assets/pdf/our-thinking/business-insights/big-data-and-investment-mangement.pdf, accessed July 2018.

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