Can AI break out of the black box?

The groundwork for modern breakthroughs in large language models was laid last century, in the revolutionary field of information theory. The future of financial AI depends on new theoretical insights that enable deeper reasoning.

© Getty Images / Alexi Rosenfeld

Artificial intelligence is transforming finance through sophisticated systems that analyze vast datasets, identify complex patterns and interpret unstructured information. This transformation can be traced to a surprising origin: the mathematician Claude Shannon’s 1948 paper on information theory. Understanding this intellectual lineage reveals not just where AI in finance came from, but where it’s likely heading — and what unique challenges lie ahead.

Finance is fundamentally about processing information: markets aggregate it, investors seek advantages from it and risk managers try to account for all of it. The tools we use to process this information shape our financial systems. As AI evolves from pattern recognition to complex reasoning, understanding its theoretical foundations becomes crucial for anticipating both its potential and its limitations.

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Author

  • Raghavendra Rau is the Sir Evelyn de Rothschild Professor of Finance at Cambridge Judge Business School. He also holds the Gresham Chair in business at Gresham College and the Ambank Chair of Financial Services at the University of Malaya. He is the incoming president of the Financial Management Association, a past president of the European Finance Association, and a past editor of Financial Management. He is a founder and director of the Cambridge Centre for Alternative Finance and a member of the Cambridge Corporate Governance Network. He also serves on the editorial boards of the Journal of Corporate Finance and the Journal of Banking and Finance, among other publications.

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