Instance-Level Explanations for Fraud Detection: A Case Study

Abstract

Fraud detection is a difficult problem that can benefit from predictive modeling. However, the verification of a prediction is challenging; for a single insurance policy, the model only provides a prediction score. We present a case study where we reflect on different instance-level model explanation techniques to aid a fraud detection team in their work. To this end, we designed two novel dashboards combining various state-of-the-art explanation techniques. These enable the domain expert to analyze and understand predictions, dramatically speeding up the process of filtering potential fraud cases. Finally, we discuss the lessons learned and outline open research issues.

arXiv commentpresented at 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden

References in corpus (4)

• Towards A Rigorous Science of Interpretable Machine Learning
• Forest Floor Visualizations of Random Forests
• Making Tree Ensembles Interpretable
• Interpreting Tree Ensembles with inTrees

Cited by in corpus (7)

• Explainable Artificial Intelligence Approaches: A Survey
• Explaining Anomalies Detected by Autoencoders Using SHAP
• Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)
• PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards
• What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems
• SUBPLEX: Towards a Better Understanding of Black Box Model Explanations at the Subpopulation Level
• A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions