Explainable Machine Learning for Fraud Detection

Explainable Machine Learning for Fraud Detection

The application of machine learning to support the processing of large data sets holds promise in many industries. We explore explainability methods in the domain of real-time fraud detection by investigating the selection of appropriate background data sets and runtime tradeoffs on supervised and u...

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Veröffentlicht in:Computer (Long Beach, Calif.) Calif.), 2021-10, Vol.54 (10), p.49-59
Hauptverfasser: Psychoula, Ismini, Gutmann, Andreas, Mainali, Pradip, Lee, Sharon H., Dunphy, Paul, Petitcolas, Fabien
Format: Artikel
Sprache:eng
Schlagworte:
Computational modeling
Data models
Datasets
Fraud
Fraud prevention
Industries
Machine learning
Real-time systems
Runtime
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Beschreibung
Zusammenfassung:The application of machine learning to support the processing of large data sets holds promise in many industries. We explore explainability methods in the domain of real-time fraud detection by investigating the selection of appropriate background data sets and runtime tradeoffs on supervised and unsupervised models.
ISSN:0018-9162
1558-0814
DOI:10.1109/MC.2021.3081249