Sarah Biller is a fintech entrepreneur and investor. She co-founded an AI-powered predictive analytics platform for institutional bond investors, Capital Market Exchange, as well as the FinTech Sandbox. Learn more about Fintech Sandbox’s Data Residency program by attending the 10th Fintech Sandbox Demo Day on April 9 and 11th.

William Feather once said, “One of the funny things about the stock market is that every time a person buys, another sells, and they both think they are astute.”

Though Feather intended to be witty, questions about where the next innovation in fintech for stock pickers is going is not out of bounds. Industry innovators are now experimenting with generative AI to translate an arguably bottomless barrel of data into ever faster, actionable analytics and trading capabilities.

Could the adoption of generative AI by financial services ironically drive a new period of information asymmetry in the markets and, by doing so, open another frontier for fintech innovation?

The answer is maybe. Capital markets investing has always been complicated and fraught. Predicting price movements necessitates that man and/or machine combine data from disparate sources that provide relevant financial variables over time, industry trends, measures of volatility, broader economic factors, and a host of other inputs into a decision framework—all while the markets, industry, and global economy continue to gyrate.

Advances in AI, natural language processing, and other technologies already increase the variety of available data and the velocity at which it can be structured. For example, companies we enable at Fintech Sandbox like applied AI firm BlueFlame minimize workflow friction and enable investors to link multimodal information sets like earnings transcripts with conversations with experts. Now, new capabilities like ChatGPT could enable investors to interrogate questions of complex, dense information more quickly and robustly.

The launch of generative AI portends a time when financial services will have systematic capabilities not just to connect the dots among disparate datasets, but have systems that adapt, learn, and possess other functionalities that drive continuous problem solving, which have long been the hallmark of astute investors.

But we must ask, do we have the computing capabilities to handle this exponential growth in the volume, variety, and velocity of data?

Undeniably, new industry entrants are racing to create the next generation of a thinking trading computer that mines huge sets of data on the edge and leverages AI to identify patterns of tradable inputs not previously observed. These inputs, coupled with complex investment algorithms, superfast trading techniques, and the progress toward enterprise-level quantum capabilities, are set to influence the capital markets in new ways. For example, Fintech Sandbox Data Residency company Boston Quantum has reimagined what enterprise computing can do to uncover market inefficiencies by matching computing power with combinatorial optimization techniques.

It's a new era for fintech innovators who can meet the opportunity and the demands of systems like generative AI and its likely future iterative capabilities for continuous problem solving. Where is the next new data coming from and what opportunities and risks lie beneath the surface? I’ll explore in future articles what this new level of data driven innovation means for fintech and the global financial services sector.