A framework for detecting credit card fraud with cost-sensitive meta-learning ensemble approach

Electronic payment systems continue to seamlessly aid business transactions across the world, and credit cards have emerged as a means of making payments in E-payment systems. Fraud due to credit card usage has, however, remained a major global threat to financial institutions with several reports and statistics laying bare the extent of this challenge. Several machine learning techniques and approaches have been established to mitigate this prevailing menace in payment systems, effective amongst which are ensemble methods and cost-sensitive learning techniques. This paper proposes a framework that combines the potentials of meta-learning ensemble techniques and cost-sensitive learning paradigm for fraud detection. The approach of the proposed framework is to allow base-classifiers to fit traditionally while the cost-sensitive learning is incorporated in the ensemble learning process to fit the cost-sensitive meta-classifier without having to enforce cost-sensitive learning on each of the base-classifiers. The predictive accuracy of the trained cost-sensitive meta-classifier and base classifiers were evaluated using Area Under the Receiver Operating Characteristic curve (AUC). Results obtained from classifying unseen data show that the cost-sensitive ensemble classifier maintains an excellent AUC value indicating consistent performance across different fraud rates in the dataset. These results indicate that the cost-sensitive ensemble framework is efficient in producing cost-sensitive ensemble classifiers that are capable of effectively detecting fraudulent transactions in different databases of payment systems irrespective of the proportion of fraud cases as compared to the performances of ordinary ensemble classifiers.