Artificial Intelligence in Financial Risk Assessment

Abstract

Artificial intelligence (AI) can support financial risk assessment by extracting patterns from large, changing data sets. In credit risk, machine learning estimates default probabilities; in fraud and anti-money-laundering operations, it ranks alerts; and in market and operational risk, it helps detect anomalies and anticipate exposures. Performance gains must be tested against calibration, robustness, fairness and operational reliability. This narrative review examines major applications and proposes a governance workflow spanning data controls, independent validation, human oversight and continuous monitoring. A transparent hypothetical credit-loss chart demonstrates how changes in predicted default probability affect a simple loss estimate; it is not empirical evidence of model accuracy. Recent supervisory and financial-stability publications emphasize that model complexity, third-party dependence and correlated decisions can introduce new vulnerabilities. AI adds value when it improves decisions under validated conditions and when the institution can explain, monitor and challenge its use.