Evaluation of explainable artificial intelligence techniques in the context of credit card fraud detection

Abstract

Artificial intelligence has been employed in several applications in the financial sector. This paper deals with one of these applications: fraud detection in credit card transactions. In this context, a number of machine learning algorithms can be used to obtain models which automate the classification of a transaction as fraudulent or genuine. However, some of these machine learning algorithms are not directly interpretable. The current paper presents an evaluation of explainable artificial intelligence techniques SHAP and LIME applied to models for fraud detection in credit card transactions. Along with the results of the evaluation, the paper discusses the effectiveness and need for explainable artificial intelligence techniques. This paper extends a previous paper by including hyperparameter tuning, new results and an evaluation of the processing time to obtain explanations. The reported results suggest that SHAP obtains better results than LIME, although LIME required less processing time after obtaining the LIME explainer.