AI for Risk Management
0 viewsAbstract
Traditional financial risk management frameworks are increasingly challenged by the speed, interconnectedness, and non-linear nature of modern market shocks. This research evaluates the efficacy of Artificial Intelligence (AI) compared to conventional econometric models across six critical dimensions: predictive modeling, volatility identification, correlation analysis, shock response, operational risk, and policy implications. Utilizing a mixed-methods approach, the study analyzes quantitative data from 120 global financial institutions (2015–2023) alongside qualitative insights from 18 senior risk officers across the US, France, and Lebanon. Quantitative results demonstrate that AI models significantly outperform traditional benchmarks; specifically, Long Short-Term Memory (LSTM) networks achieved a 47.5% lower out-of-sample error in volatility prediction compared to GARCH(1,1), while XGBoost classifiers provided superior credit default precision during the 2022 rate-hiking cycle. Furthermore, Vector Autoregression (VAR) and Impulse Response Function (IRF) analysis revealed that AI-integrated institutions exhibited a 37.7% greater buffer in shock transmission. Despite these technical advantages, qualitative findings highlight "black box" interpretability and regulatory alignment (e.g., Basel IV, EU AI Act) as primary barriers to adoption. The study concludes that while AI offers a prospective rather than retroactive paradigm for financial stability, its successful deployment requires a hybrid architecture that balances predictive power with institutional explainability.
Keywords
Artificial Intelligence, Explainable AI (XAI)., Financial Risk Management, Machine Learning, Predictive Modeling, Regulatory Technology (RegTech), Systemic Stability