Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models

TL;DR

This paper introduces a case-based reasoning approach that visualizes similar past instances to help fraud analysts assess the trustworthiness of machine learning predictions, improving interpretability and usability.

Contribution

It presents a novel CBR method that considers local explanation similarity, aiding domain experts in understanding and trusting ML alerts for fraud detection.

Findings

Visualization aids fraud analysts in decision-making

Approach improves interpretability of ML predictions

Perceived as useful and user-friendly by domain experts

Abstract

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.