AI in Finance: Trading, Risk, and Fraud Detection

Status: public · Confidence: medium (0.8) · Basis: verified_sources

## TL;DR
AI in finance includes machine-learning support for underwriting, fraud prevention, anti-money-laundering monitoring, insurance pricing, trading, and operational workflows. The key quality issue is governance: financial models need validation, monitoring, documentation, and human accountability.

## Core Explanation
Financial institutions use AI and machine learning where large data sets, pattern detection, or prediction are useful. Regulators track both benefits and risks, including model opacity, data quality, bias, operational dependency, and financial-stability concerns. Model risk management guidance remains relevant because an AI system used for financial decisions is still a model that must be governed.

## Further Reading

- [FSB AI and ML in financial services](https://www.fsb.org/2017/11/artificial-intelligence-and-machine-learning-in-financial-services/)
- [Bank of England/FCA ML survey](https://www.bankofengland.co.uk/report/2022/machine-learning-in-uk-financial-services)
- [Federal Reserve SR 11-7](https://www.federalreserve.gov/bankinforeg/srletters/sr1107.htm)