Accurate fraud detection in credit card transactions using hybrid heuristic and meta-heuristic algorithms

Abstract

Credit card based online transactions, payment methods developed rapidly, Credit card fraud also additionally expanded simultaneously. The particular crime in banking system is credit card fraud. The main objective of proposed AFD-HHM technique is, to detect the fraudulent transaction with high accurate rate and solve the user side problems. Some most dangerous credit card crimes are credit card application fraud, phishing scams, credit card skimming, online sales fraud, Credit Card Imprints, which leads to following user problems such as class imbalance problem, verification latency and concept drift. This paper focus to detect the fraud transactions with higher efficeiency rate solves the user side problems, high computational complexity problems. Initially, we propose Improved Egyptian Vulture Optimization (IEVO) algorithm (meta-heuristic) to grouping all cardholders into different groups based on the transaction behaviours. Second, we can aggregate each group transactions and compute the optimal attributes for each cardholder by using of trust rule strategy. Third, we propose Differential Evolution based Neural Network (DENN) classifier for differentiate transaction as normal or fraud. The result of proposed approach gives better accuracy with higher rate which is comparing with existing methods.