Artificial Intelligence in Financial Risk Management

Abstract

Financial institutions are exposed to an increasing complex and fast-paced credit risk, market risk, liquidity risk, operational risk and fraud risk. Artificial intelligence (AI), machine learning, deep learning and natural language processing are providing new tools to more accurately measure and manage these risks than traditional statistical models. In this paper, we overview the use of AI in the primary risk areas and introduce a conceptual framework relating AI adoption and risk management effectiveness, along with an empirical example that compares model families in terms of their performance in credit default prediction, fraud detection, and market risk forecasting. The illustrative results demonstrate that the ensemble and neural models, as compared to logistic regression, provide better discrimination and fewer false positives without relaxing the constraints of explainability, model risk, data bias, or regulatory expectations. The paper concludes with a governance roadmap and recommendations for practitioners and regulators. Note: all numbers in the tables are examples and indicate the analysis structure, and must be changed to your experimental or institutional numbers prior to submission.

key words : AI, machine learning, credit risk, fraud detection, market risk, model risk, explainable AI.