article · Scientific African
Credit card transactions are vital for electronic payment systems, but fraudulent activity poses a persistent global threat to financial institutions. While machine learning helps mitigate this challenge, fraud datasets often suffer from severe class imbalances. A framework addresses this issue by uniting ensemble meta-learning with a cost-sensitive learning paradigm. Rather than enforcing cost-sensitive learning across individual base classifiers, the system trains base models conventionally and integrates cost sensitivity directly into the meta-classifier stage. When evaluated on unseen transaction data using the Area Under the Receiver Operating Characteristic curve, the resulting ensemble demonstrated strong predictive accuracy. The model maintained high performance across various fraud rates, demonstrating an ability to identify fraudulent transactions across diverse payment system databases more effectively than standard ensemble classifiers.
Credit card fraud creates substantial financial losses for institutions globally, yet detecting rare fraudulent events remains difficult for standard algorithms. By accounting for the unequal costs of errors without complicating the training of base models, this approach offers a dependable way to flag fraudulent transactions across different payment systems, regardless of how rarely fraud occurs within the data.
This methodology is designed for financial institutions and electronic payment operators seeking to improve automated fraud monitoring. The findings could inform backend detection engines used in transaction processing platforms. Based strictly on the abstract, the framework represents applied and tested research evaluated on unseen data, but it does not yet indicate live deployment or direct integration into operational banking pipelines.
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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.
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DOI: 10.1016/j.sciaf.2020.e00464
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