Topic
AI in Capital Markets
Capital markets run on numbers that must be reproducible, explainable, and defensible to a regulator. Here is where probabilistic AI helps, where deterministic analytics has to hold the line, and how the two work together.
Explore AI in Capital Markets
Deterministic vs Probabilistic Quick Explainer
Around the numbers, not inside them. Probabilistic models are strong at reading documents, finding patterns, and speeding up research. The price, the sensitivity, and the capital figure still have to come from methods a model validator can reproduce and an auditor can trace.
Only if it can be reproduced and explained. Model risk guidance such as SR 11-7 asks for documented methodology, independent validation, and ongoing monitoring. A deterministic model supports that directly. A model that gives different answers to the same question needs far more control around it before it can carry a number of record.
Because the randomness in Monte Carlo is controlled. Seeds are fixed, convergence is measured, and the error is quantified, so a result can be repeated and audited. The test is reproducibility and bounded error, not whether randomness is involved.
Use AI to approximate an expensive calculation and keep the trusted model as the reference. Benchmark the approximation against it, monitor the gap, and fall back to the reference when the gap widens. The result is faster answers with a deterministic benchmark behind them.
Applied AI
How firms apply machine learning, generative AI, and agentic AI to pricing, risk, and research in capital markets.
Responsible AI
How firms validate, govern, and secure AI in capital markets, from model risk and regulation to fraud prevention and trust.
