Automatic Credit Fraud Detection Using Ensemble Model

Abstract

AbstractAs dependency on credit cards and online transactions is increasing, the scope for unauthorized payments or fraudulent transactions is on the rise. Huge financial losses are borne by banks every year due to fraudulent transactions. The process of identifying such transactions among millions of transaction records can be perfected using machine learning models, even more so by ensembling them. This study takes a real-time financial dataset and analyzes the efficiency of various machine learning models in predicting fraudulent transactions. Individual ML models are also analysed, ensembled and tested on the dataset. Upon extensive analysis and research, it is concluded that an ensembled model of LightGBM, decision tree classifier and XGBoost gave the highest AuC score of 87%. Further, a user-interactive tool is also created, which predicts fraudulent transactions from real-time transactions. The tool makes use of the model with highest prediction efficiency and presents it in an interactive manner. This study and practical demonstration show advancement in prediction of fraudulent transactions when compared to existing ML models and its user-friendly software incorporation.KeywordsFraud detectionEnsembleAccuracyFraudulent transactionBankingMachine learning model