Explainable Machine Learning for Fraud Detection

• URL: http://arxiv.org/abs/2105.06314v1
• Date: Thu, 13 May 2021 14:12:02 GMT
• Title: Explainable Machine Learning for Fraud Detection
• Authors: Ismini Psychoula, Andreas Gutmann, Pradip Mainali, S. H. Lee, Paul Dunphy, Fabien A. P. Petitcolas
• Abstract summary: The application of machine learning to support the processing of large datasets holds promise in many industries, including financial services. 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.
• Score: 0.47574189356217006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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