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arXiv · 2105.06314

Explainable Machine Learning for Fraud Detection

Ismini Psychoula·Andreas Gutmann·Pradip Mainali·S. H. Lee·Paul Dunphy·Fabien A. P. Petitcolas
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Abstract

The application of machine learning to support the processing of large datasets holds promise in many industries, including financial services. However, practical issues for the full adoption of machine learning remain with the focus being on understanding and being able to explain the decisions and predictions made by complex models. In this paper, we explore explainability methods in the domain of real-time fraud detection by investigating the selection of appropriate background datasets and runtime trade-offs on both supervised and unsupervised models.

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Ismini Psychoula, Andreas Gutmann, Pradip Mainali, S. H. Lee, Paul Dunphy, Fabien A. P. Petitcolas. 2021-05-13. Explainable Machine Learning for Fraud Detection. https://arxiv.org/abs/2105.06314

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