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Explainable Artificial Intelligence in Risk Management: A Framework

In: Artificial Intelligence and Beyond for Finance

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  • Silvio Andrae

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.

Suggested Citation

  • Silvio Andrae, 2024. "Explainable Artificial Intelligence in Risk Management: A Framework," World Scientific Book Chapters, in: Marco Corazza & RenĂ© Garcia & Faisal Shah Khan & Davide La Torre & Hatem Masri (ed.), Artificial Intelligence and Beyond for Finance, chapter 4, pages 149-206, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9781800615212_0004
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    More about this item

    Keywords

    Artificial Intelligence; Machine Learning; Deep Learning; Reinforcement Learning; Sentiment Analysis; Portfolio Management; Financial Forecasting;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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