Explainable Machine Learning for Fraud Detection

Autor:	Psychoula, Ismini, Gutmann, Andreas, Mainali, Pradip, Lee, S. H., Dunphy, Paul, Petitcolas, Fabien A. P.
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: To be published in IEEE Computer Special Issue on Explainable AI and Machine Learning, 12 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.06314 Zobrazit plný text záznamu View this record from Arxiv