Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models
This work addresses the need for domain experts, specifically fraud analysts in banking, to better interpret and act on machine learning-generated alerts, though it is incremental as it builds on existing case-based reasoning and explanation methods.
The paper tackled the problem of helping fraud analysts assess the trustworthiness of black-box machine learning model predictions for fraud alerts by developing a case-based reasoning approach that visualizes similar past instances based on local explanations, showing empirically that it aids in processing alerts and is perceived as useful and easy to use by analysts at a major bank.
In many contexts, it can be useful for domain experts to understand to what extent predictions made by a machine learning model can be trusted. In particular, estimates of trustworthiness can be useful for fraud analysts who process machine learning-generated alerts of fraudulent transactions. In this work, we present a case-based reasoning (CBR) approach that provides evidence on the trustworthiness of a prediction in the form of a visualization of similar previous instances. Different from previous works, we consider similarity of local post-hoc explanations of predictions and show empirically that our visualization can be useful for processing alerts. Furthermore, our approach is perceived useful and easy to use by fraud analysts at a major Dutch bank.