Explainable AI Framework for Credit Card Fraud Detection Using Hybrid Machine Learning Models

The rapid growth of digital payment systems has significantly increased the sophistication and frequency of credit card fraud, making accurate and interpretable fraud detection models a critical requirement for financial institutions. While advanced machine learning algorithms have demonstrated high predictive performance, their limited interpretability often restricts adoption in highly regulated financial environments where transparent decision-making is essential. This paper proposes an Explainable Artificial Intelligence (XAI) framework that integrates hybrid machine learning models with explainability techniques to improve fraud detection while maintaining regulatory transparency. The proposed framework combines supervised learning algorithms, including Random Forest, Gradient Boosting, and Extreme Gradient Boosting (XGBoost), with feature attribution methods such as SHAP and LIME to provide interpretable explanations for fraud predictions. In addition, data preprocessing techniques addressing class imbalance and feature engineering are incorporated to enhance predictive performance. Experimental evaluation on publicly available credit card transaction datasets demonstrates that the hybrid framework achieves improved detection capability while enabling analysts to understand the key factors influencing model decisions. The proposed approach supports compliance with emerging regulatory expectations for explainable artificial intelligence in financial services and assists fraud investigators in making informed operational decisions. The framework contributes toward building trustworthy AI systems capable of balancing prediction accuracy with transparency in modern digital payment ecosystems.

Explainable Artificial Intelligence, Credit Card Fraud Detection, Machine Learning, XAI, SHAP, LIME, XGBoost, Financial Analytics

J. A. Gromicho, J. J. Van Hoorn, F. Saldanha-da Gama, and G. T. Timmer, “Solving the job-shop scheduling problem optimally by dynamic programming,” Comput. Oper. Res., vol. 39, no. 12, pp. 2968–2977, 2012.

P. Kumar and M. Kumar, “Production scheduling in a job shop environment with consideration of transportation time and shortest processing time dispatching criterion,” Int. J. Adv. Eng. Res. Appl., vol. 1, no. 1, pp. 1–9, 2015.

X. N. Shen and X. Yao, “Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems,” Inf. Sci., vol. 298, pp. 198–224, Mar. 2015.

C. Soto, B. Dorronsoro, H. Fraire, L. Cruz Reyes, C. Gomez Santillan, and N. Rangel, “Solving the multi-objective flexible job shop scheduling problem with a novel parallel branch and bound algorithm,” Swarm Evol. Comput., vol. 53, Mar. 2020, Art. no. 100632.

L. N. Xing, Y. W. Chen, P. Wang, Q. S. Zhao, and J. Xiong, “A knowledge-based ant colony optimization for flexible job shop scheduling problems,” Appl. Soft Comput., vol. 10, no. 3, pp. 888–896, 2010.

M. Xu, Y. Mei, F. Zhang, and M. Zhang, “Genetic programming for dynamic flexible job shop scheduling: Evolution with single individuals and ensembles,” IEEE Trans. Evol. Comput., vol. 28, no. 6, pp. 1761–1775, Dec. 2024.

M. Xu, F. Zhang, Y. Mei, and M. Zhang, “Genetic programming with multi-case fitness for dynamic flexible job shop scheduling,” in Proc. IEEE Congr. Evol. Comput., 2022, pp. 1–8.

T. Yang, Z. He, and K. K. Cho, “An effective heuristic method for generalized job shop scheduling with due dates,” Comput. Ind. Eng., vol. 26, no. 4, pp. 647–660, 1994.

W. Yu, L. Zhang, and N. Ge, “An adaptive multiobjective evolutionary algorithm for dynamic multiobjective flexible scheduling problem,” Int. J. Intell. Syst., vol. 37, no. 12, pp. 12335–12366, 2022.

F. Zhang, Y. Mei, S. Nguyen, and M. Zhang, “Survey on genetic programming and machine learning techniques for heuristic design in job shop scheduling,” IEEE Trans. Evol. Comput., vol. 28, no. 1, pp. 147–167, Feb. 2024.

C. Zhang, Y. Rao, and P. Li, “An effective hybrid genetic algorithm for the job shop scheduling problem,” Int. J. Adv. Manuf. Technol., vol. 39, pp. 965–974, Jul. 2008.

R. Zhang, S. Song, and C. Wu, “A two-stage hybrid particle swarm optimization algorithm for the stochastic job shop scheduling problem,” Knowl. Based Syst., vol. 27, pp. 393–406, Mar. 2012