Building AI Systems for Capital Markets

Key takeaways:

The "demo versus product" gap:  An AI demo is judged on its best day, but an institutional-grade product is judged on its worst. Building reliable financial tools requires rigorous, auditable grounding frameworks to avoid hallucinations and ensure every output can be traced back to a verified source.

Don't bet against the model:  Developers should avoid wasting resources on complex workarounds for temporary constraints on models that are evolving rapidly. Instead, they should focus on elements that models cannot natively learn: details specific to the firm, such as proprietary data or how data sets are connected to each other.

Tackling legacy constraints: Successful innovation lies in redesigning workflows from first principles rather than simply automating legacy bottlenecks to do the same tasks faster.

In this episode of Goldman Sachs Exchanges, Chris Churchman, head of Marquee, Goldman Sachs’ digital platform for institutional and corporate clients, talks about building effective artificial intelligence (AI) products for institutional investors.

Churchman, who is also co-chair of the Global Banking & Markets AI Working Group, describes an ongoing shift from a world where users must learn software to one where software learns the user. He tells hosts Allison Nathan of Goldman Sachs Research and George Lee, co-head of the Goldman Sachs Global Institute, about the challenges of grounding generative AI in hard facts, and emphasizes the need to design systems that enhance human reasoning rather than outsourcing critical thinking to machines.

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