An online fuzzy fraud detection framework for credit card transactions

Credit card transaction fraud is one of the challenging security concerns for financial firms globally. The continuously changing environment of fraud characteristics and the class imbalance and complete separation issues in fraud data create difficulties in accurately and efficiently predicting fraudulent transactions and implementing fraud detection systems in real-time. The study aims to develop a new real-time fraud detection framework that can efficiently be implemented online and address the issues caused by non-stationary changes in transaction and fraud characteristics, class imbalance, and complete separation. We propose a new approach to handle the impact of non-stationary changes in fraud transaction patterns. It enables efficiency in model training, given the sheer size of datasets. By implementing a robust fuzzy logistic regression model against class imbalance and separation problems, we address the challenge of having a very low rate of fraudulent transactions in the dataset and having separation issues due to specific characteristics of transactions. The analysis of the performance versus efficiency nexus of the proposed methodology reveals that the proposed framework shows strong performance results with specificity and sensitivity greater than 0.90 and Matthew's correlation coefficient greater than 0.80 even on small sample sizes and produces highly accurate results in identifying fraudulent and non-fraudulent transactions with an accuracy greater than 0.99. Benchmarking with machine learning and other fraud detection approaches reveals that the proposed framework provides better detection performance while maintaining a higher rate of identifying non-fraudulent transactions than the alternative approaches. Improved classification performance leads to detecting fraudulent transactions with higher precision while avoiding misclassifying legitimate transfers, resulting in lower financial losses and improved customer satisfaction.