Credit Card Fraud Detection with LIME and SHAP
Pavuluri Swetha · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: The rate of credit card theft has increased in recent years due to the widespread adoption of advanced technology and worldwide communication networks. Scammers are always searching for novel techniques to engage in unlawful activities, irrespective of the fact that credit card fraud results in billions of dollars in losses for individuals and financial institutions annually. Therefore, the absence of fraud detection technologies significantly hampers the successful functioning of banks and other financial organisations. The objective of the project is to create a credit card fraud detection system using machine learning models such as XGBoost and Decision Tree. The accuracy of these models will be assessed using LIME and SHAP models, which provide explanations for the chosen models. Ultimately, a comparison examination of the machine learning models will be conducted.