Abstract
Machine learning and artificial intelligence have become increasingly attractive for quantitative risk management in recent years. Today’s significantly enhanced computer power paves the way for the use of more complex models (e.g., artificial networks with a considerable number of nodes), which results in a much higher quality of results. Both the achievable enhanced quality of results as well as an enhanced familiarity with the methodology result in an increasing acceptance of these methods by financial regulators. In this article, we not only address the aspect of reliability of the results obtained using machine learning methods, but also indicate the impact on how credit risk parameters are modeled.
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Notes
- 1.
More parameters such as expected and unexpected loss could be mentioned, but we focus on a simple example here.
- 2.
sklearn.model_selection.train_test_split().
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Thiele, M., Dittmar, H. (2019). Internal Credit Risk Models with Machine Learning. In: Liermann, V., Stegmann, C. (eds) The Impact of Digital Transformation and FinTech on the Finance Professional. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-23719-6_10
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DOI: https://doi.org/10.1007/978-3-030-23719-6_10
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