Publication

Abstract : The most popular electronic payment method is a credit card, which is more susceptible to theft due to the rising number of daily electronic transactions. Credit card frauds have cause credit card companies incur huge financial losses. To develop an effective credit scoring model is very imminent to prevent this fraud. In order to support the financial decisions made by banks and other financial organizations, researchers have created sophisticated credit scoring models using statistical and machine-learning techniques. Thus, the main aim of this paper is to help the bank management to develop models and predict consumer behavior on the basis of real time demographic data for credit card issuance. The research also exhibits how to treat data imbalance problem using Synthetic Minority Oversampling Technique (SMOTE) after applying various statistical tests. Different prediction models like Linear Regression, Decision Tree, XGBoost, AdaBoost, Random Forest etc. are also explored and applied on data to pick the best optimized one giving 92.03% accuracy and 97.32% Area Under the ROC Curve (AUC). The relevant parameters which are actually responsible for the identification of credit card fraud are highlighted by applying Weight of Evidence (WoE) and Inflation Variance (IV) techniques to all independent variables, which are able to find parameters having strong predicting power. The findings of such an experimental study can be really useful to bank managers to issue credit cards to customers.