Explainable Artificial Intelligence in Risk Management: A Framework

Silvio Andrae

Chapter 4 in Artificial Intelligence and Beyond for Finance, 2024, pp 149-206 from World Scientific Publishing Co. Pte. Ltd.

Abstract: It is easier than ever to run modern machine learning (ML) models. However, developing and implementing systems that support real-world risk management applications in a bank is a significant challenge. It is partly because ML models are not transparent and explainable. The framework presented here covers the leading eXplainable AI (XAI) methods. Practical challenges in implementing these methods are discussed.

Keywords: Artificial Intelligence; Machine Learning; Deep Learning; Reinforcement Learning; Sentiment Analysis; Portfolio Management; Financial Forecasting (search for similar items in EconPapers)
JEL-codes: C63 C8 G11 G17 (search for similar items in EconPapers)
Date: 2024
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