Menglin Cao, Senior VP and Head of AI and NLP Model Development at Wells Fargo, discusses the central role of data science in fintech and financial services, and best practices for success in her WiDS podcast. Over the past 15 years, she has seen a major shift in how financial institutions use data to drive business decisions. In the past, many business decisions were made based upon the experience and judgement of senior executives, but today every decision must be backed up by data and analytics. Many aspects of financial services that leverage data are great candidates for AI and ML models, such as customer experience, revenue generation, and risk management. To be useful, the data must first be focused on a foundational business question. The journey to find that answer requires data science, algorithms, operational procedures, implementations, and integrations of systems, but without the right question to begin with, it doesn’t lead anywhere.
In her WiDS presentation, When Data Science is the Business, Leda Braga, CEO of Systematica Investments, explains that systematic investment management is data science applied to investment. She says this approach is at least on par with the historical human approach but is more scalable and disciplined. Data science plays a role in signal generation (deciding what securities to invest in and forecasting), portfolio construction, and trade execution. She says diversification is a primary difference between the systematic approach and discretionary approach to investment. The systematic approach makes the investment process less reliant on the random nature of forecasting and more reliant on risk control in portfolio construction. She says we’re not yet at a point where AI allows us to do “autonomous investing” because there's a large element of randomness in markets and relatively sparse data so learning algorithms have limited use. Braga is a strong believer in ESG investment as a powerful lever to drive accountability in the markets.
Susan Athey, Economics of Technology Professor at the Stanford Graduate School of Business, brings an economist’s perspective to data science in her WiDS podcast. With a career spanning academia and industry, Susan’s research focuses on the economics of digitization, marketplace design, and the intersection of econometrics and machine learning. She brings a social science perspective to AI questions and explains how you need to understand the system and the economic incentives of the people operating in the system. While an engineering perspective sees a database full of advertiser bids that feels static, an economist’s perspective sees those bids as strategic. If you understand the behavior of those firms, and their objectives, you can predict their responses to a change in the system. This is why it’s so important to bring in multiple perspectives. Athey was one of the first economists to take bitcoin seriously and sees it as a technology that can help address inequality. She says many people in the world are disadvantaged by an archaic financial system that is operated for the benefit of large businesses and banks in large countries. “If we can move money the way that we can move information, we could actually make a lot of people's lives better off.”