Here are the AI developments that finance pros should be tracking

Artificial intelligence and machine learning are rapidly redefining the financial landscape, unlocking new opportunities but also introducing complex challenges for financial institutions, investors, and regulators.

“This is definitely not business as usual,” said Andrew W. Lo, an MIT Sloan professor of finance and director of the MIT Laboratory for Financial Engineering. “We’re living through an inflection point in technology, but what exactly is that inflection point? And when and how will it impact specific business lines and companies?”

Lo’s new executive education class — Artificial Intelligence for Financial Services: Tools, Opportunities, and Challenges — is designed to help decision makers navigate the changing landscape. The course, which features a cross-disciplinary group of faculty members and experts, covers practical applications across the buy and sell sides and the banking, insurance, and risk management sectors.

In a recent conversation, Lo outlined some of the topics that he believes financial professionals should be tracking now:

• The evolving relationship between machine learning and large language models. Machine learning is “a well-established tool that is now being reshaped by the emergence of large language models,” Lo said. LLMs can help interpret the outputs of machine learning models, making them more transparent and actionable for investment decision makers.
• The rise of “quantamental investing” — that is, the hybrid investment approach that combines quantitative and fundamental investment styles and strategies. Quantitative investing uses computer models, algorithms, and data to identify trends and patterns, whereas fundamental investing analyzes a company’s underlying financial health using a more qualitative approach. “Large language models have created the opportunity for developing a powerful hybrid approach” that combines the best of both investment styles, Lo said.
• The challenge of interpreting and trusting LLMs in high-stakes applications. LLMs are trained to convey confidence in their outputs, regardless of whether those outputs are correct. When an LLM produces a financial forecast or a sentiment signal, financial professionals need to know how the model arrived at its conclusion and whether its output can be trusted.
• The impact of AI on market dynamics, investment strategies, and risk management. Advances in data and algorithmic techniques are reshaping how financial institutions identify opportunities, allocate capital, and manage risk, with implications for both market behavior and competitive advantage.
• The practical and economic challenges of deploying AI in financial institutions. Moving from experimentation to production requires integrating models into workflows, managing unstructured data, and assessing whether AI applications deliver meaningful productivity gains.
• AI governance, transparency, and regulation. As AI becomes integrated into financial decision-making — spanning credit scoring, trading, and fraud detection — it raises questions of accountability. When failures occur, determining responsibility becomes difficult, and regulators often struggle to verify how decisions were made. Designing systems that are inherently accountable is the most important challenge to overcome to unlock widespread AI adoption in the financial industry, Lo said.

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In sum, Lo’s course aims to give participants an idea of where AI and financial technology are headed in the next five years so that they can better assess how new tools and technologies may reshape products, markets, and organizational capabilities.

“We need to understand not only the pace of progress but also ways to extrapolate the impact of AI on our professional and personal lives,” Lo said. “There will be big changes coming down the pike.”

Andrew W. Lo is the Charles E. and Susan T. Harris Professor at the MIT Sloan School of Management and the director of the MIT Laboratory for Financial Engineering. His recent projects include an evolutionary model of financial markets based on his Adaptive Markets Hypothesis; new financing methods and business models for accelerating biomedical innovation; quantitative approaches to deep-tech investing; applying AI, especially machine learning and LLMs, to financial advice; quantamental investing; and health care finance. His most recent book is “The Adaptive Markets Hypothesis: An Evolutionary Approach to Understanding Financial System Dynamics.”