Comparison of explainable artificial intelligence techniques in detecting fraud in credit card transactions

• Gabriel Mendes de Lima UFABC
• Paulo Henrique Pisani UFABC

DOI: https://doi.org/10.5753/sbseg_estendido.2024.243180

Abstract

Intelligent systems are used in the financial sector, including for fraud detection. In credit card transactions, machine learning algorithms can be used to obtain models which automate decisions such as classifying a transaction as fraudulent or not. In this context, this work presents a comparison between the explainable artificial intelligence techniques SHAP and LIME in models for fraud detection in credit card transactions, showing that these techniques can be suitable for the problem. The use of interpretable algorithms in critical sectors such as financial sector is also discussed, as well as the effectiveness and need for explainable artificial intelligence techniques.

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