A Systematic Survey and Empirical Comparison of Hybrid Methods for Imbalanced Fraud Detection: Combining Resampling and Machine Learning
Our paper “A Systematic Survey and Empirical Comparison of Hybrid Methods for Imbalanced Fraud Detection: Combining Resampling and Machine Learning” by Behnam Yousefimehr; Mehdi Ghatee has been published in AUT Journal of Mathematics and Computing.
The abstract of this paper is as follows:
The accurate identification of fraudulent activities has been a significant focus of computational research, leading to the development of diverse methodologies ranging from traditional statistical tests to advanced machine learning and deep learning models. A persistent and critical challenge undermining these approaches is the inherent class imbalance present in most real-world fraud datasets, where genuine transactions vastly outnumber fraudulent ones, often causing models to exhibit bias toward the majority class. To mitigate this issue, a promising paradigm has emerged: hybrid frameworks that synergistically integrate data resampling techniques with robust machine learning algorithms. These frameworks are particularly valuable for their potential to facilitate accurate, real-time automated detection systems. This survey provides a comprehensive examination of the efficacy and impact of such hybrid techniques on the field of fraud detection. To quantitatively evaluate their performance, we conduct a rigorous numerical study using auto insurance fraud as a case study. Employing the Car fraud datasets, we perform a detailed comparative analysis of various detection algorithms, each coupled with different resampling methods. Our empirical results demonstrate that the performance of each fraud detection algorithm is profoundly contingent upon the specific resampling strategy employed, highlighting the necessity for careful methodological selection tailored to the dataset’s characteristics. Code for analysis is available at
The DOI of this paper is 10.22060/ajmc.2025.24642.1446.
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You can refer to this paper as “Yousefimehr, B., Ghatee, M. (2026). ‘A systematic survey and empirical comparison of hybrid methods for imbalanced fraud detection: Combining resampling and machine learning’, AUT Journal of Mathematics and Computing, 7(1), pp. 85-116. doi: 10.22060/ajmc.2025.24642.1446