Natural Language Generation: Sailing Through Your Data Lakes

While most might not admit it, many wealth managers are drowning in their data lakes.

Whether structured or unstructured data, performance, or regulatory information, corporate or client documents, it is easy for firms with a large number of accounts to feel like they can’t get their head above water to make the best decisions for their clients.

Many firms have basic checklists and a standardization process for their data that focus primarily on accuracy and reliability. However, with the help of artificial intelligence (A.I.) and natural language generation (NLG), there is a way to make the data work for you and empower your strategy and decision-making.

If you are a wealth manager managing 500 or more accounts, that may only allow you 30 minutes a month of mindshare to analyze the data per portfolio. That is, unless your firm is investing in expanding your tech stack with tools that can analyze and process the data you need.

What is Natural Language Generation?

Before establishing NLG, we must first identify natural language processing (NLP), which is an A.I. technology that converts textual data into predictions and classifications in the form of numbers.

Natural language generation (NLG) is a part of NLP that turns structured data into written or spoken language. When these technologies are combined, they have the capabilities to analyze the collected data, interact with this data, and extract the underlying meaning and insights to describe it in the form of written narratives.

As per recent research, the global natural language generation market was valued at more than USD $336 million in 2018, and it is expected to rise at an annual rate of 19.8 percent from 2019 to 2025.

Out of the total market share, the financial services, insurance, and banking sectors accounted for nearly 22 percent of the market in 2018. The report also states that by 2025, the banking and financial services segments are expected to dominate in terms of the overall NLG market share.

How Can Wealth Managers Leverage NLG and Machine Learning?

• Investment Analysis: Actionable Narratives for Strategic Data-Driven Decision Making

As we have mentioned, many wealth managers lack the bandwidth and resources to individually track portfolios and accounts to a high level of detail.

However, with NLG and machine learning, they have the ability to track the impact of current or evolving trends in the market, unveil potential investment opportunities, spot risk outliers, and predict market downturns. This in-depth and personalized analysis adds a higher level of value to the portfolio analysis and is invaluable to the strategy and decision-making process.

• Increase Personalization and Deepen Client Relationships

Studies show that organizations outperforming their competition attribute 40 percent of the additional revenue to their personalization efforts.

The ability to speak into individual portfolios with a higher level of detail and data-driven strategies builds trust and loyalty between the advisor and their clients. Using NLG to provide insights into a client’s account to provide accurate and personal advice can strengthen an advisor’s ability to show value.

• Reduce Operational Costs and Drive Business Growth

NLG and A.I. will never replace the “human in the loop,” but it can significantly decrease the time needed and spent on data analysis and the investment strategy process.

With every area of financial services having to analyze and report some sort of data, NLG can be put to work to automate repetitive, time-consuming workflows and increase the quality, speed, and consistency of analytics and reporting.

The narratives generated by NLG can be used by the chief investment officers, data analysts, portfolio managers, and compliance teams to gain an advantage over their competitors. This way, the data analysts and executives can devote their time on other value-added tasks that drive business growth.

Whether your firm has mastered the data extraction, reconciliation, and normalization phases of data aggregation or not, it still leaves you with a massive vault of information that needs context, analysis, and review.

(Editor’s note: The author Nayan Madhamshettiwar is a managing director,  professional services for First Rate, Inc. Before his role at First Rate, Madhamshettiwar served as assistant vice president, A.I. implementation leader, capital markets at Genpact. He also served as a director at Rageframeworks, overseeing business development, operations, and implementation. His expertise extends to crafting investment portfolio strategies through the application of A.I. He has deployed an A.I.-based long-and short-term memory model, enhancing risk-adjusted returns for pension funds, endowment funds, UHNWI, and individual clients. Nayan will expand on this topic further during his presentation at FTF’s Performance Measurement & Client Reporting event taking place on Feb. 29, 2024, at Etc. Venues, 601 Lexington Ave. in New York City during the "Riding the A.I. Wave to New Shores" session.)