A predictive model to minimize false positive declines in Electronic Card Not Present financial transactions using feature engineering techniques

Abstract

In this research, we have proposed a predictive model to minimize false positive (“Legitimate transactions are being declined falsely identifying as fraudulent”) declines in electronic CNP transactions. Related to the increased popularity of digital payments FP declines are becoming a severe problem among merchants who provide digital payment solutions. It’s estimated that nearly 10% of the transactions are been declined as fraudulent transactions but only very few of them have fallen into the fraud category. To address this problem we have proposed a feature engineering technique based on behavior analysis. Our research is conducted based on a reallife CNP transactional data set from one of the largest fintech service solution providers in Sri Lanka and we have generated 130 features for each transaction and have employed an XG Boost to learn the classifier and found out that performances of the xgBoost classifier has shown nearly 6% improvement in the F-Score and obtained 0.996 for the AUC after the application of feature engineering techniques. We found out that this solution can mainly benefit the merchants who provide electronic payment solutions which involve CNP transactions to minimize false-positive declines targeting legitimate frequent customers and by the same, it minimizes the fraud losses and protects the customer’s interests.