Reliable Logistic Regression for Credit Card Fraud Detection

credit card fraud poses a significant threat to financial institutions and consumers worldwide, necessitating robust and reliable detection methods. Traditional classification models often struggle with the challenges of imbalanced datasets, noise, and outliers inherent in transaction data. This paper introduces a novel fraud detection approach based on a discrete non-additive integral with respect to a non-monotonic set function. This method not only enhances classification performance but also provides an interval-valued output that serves as an index of reliability for each prediction. The width of this interval correlates with the prediction error, offering valuable insights into the confidence of the classification results. Such an index is crucial in high-stakes scenarios where misclassifications can have severe consequences. The model is validated through extensive experiments on credit card transaction datasets, demonstrating its effectiveness in handling imbalanced data and its superiority over traditional models in terms of accuracy and reliability assessment. However, potential challenges such as increased computational complexity and the need for careful parameter tuning may affect scalability and real-time implementation. Addressing these challenges could further enhance the practical applicability of the proposed method in fraud detection systems.