Explainable Machine Learning for Fraud Detection
This work addresses the need for explainable AI in financial services to improve adoption of machine learning for fraud detection, but it appears incremental as it builds on existing explainability methods.
The paper tackled the challenge of making machine learning models understandable and explainable in real-time fraud detection, focusing on selecting appropriate background datasets and runtime trade-offs for both supervised and unsupervised models.
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.