Explainable Cloud-Native Payment Fraud Detection Using SHAP-Guided Feature Adaptation

Sanne de Vries; Felipe Rocha

doi:10.54097/12v3ax90

Authors

Sanne de Vries Informatics Institute, University of Amsterdam, Netherlands
Felipe Rocha Informatics Institute, University of Amsterdam, Netherlands

DOI:

https://doi.org/10.54097/12v3ax90

Keywords:

Fraud detection, Explainable artificial intelligence, SHAP, Feature selection, Cloud-native systems, Model interpretability, Concept drift, Real-time inference, Imbalanced classification

Abstract

Real-time payment fraud detection in cloud-native environments must reconcile three competing pressures: predictive accuracy, strict tail-latency budgets, and growing regulatory demands for explainability. Prior work has mapped the accuracy-latency-footprint frontier for CPU-friendly classifiers but has not treated explainability, nor the runtime cost of the feature pipeline itself, as first-class design concerns. We introduce SHAP-Guided Feature Adaptation (SGFA), a teacher-student method that (1) trains a random-forest teacher on all features, (2) computes exact TreeSHAP attributions on a validation window to obtain a faithful global feature ranking, (3) selects a compact feature subset through a transparent rule—the smallest number of features reaching ninety percent of the teacher’s best validation PR-AUC—and (4) retrains a lightweight student on that subset. On the public ULB credit-card dataset (284,807 transactions, 0.173% fraud) under a chronological split and a precision-floor operating point, SGFA shrinks the feature set from thirty to eight (a 73% reduction) while a logistic-regression student retains 96.5% of the full-feature PR-AUC at a p99 latency of 321 microseconds and a 1.3 KB memory footprint; fifteen features fully recover full-feature accuracy. We further show that the SHAP ranking is competitive with the strongest filter baseline while additionally yielding faithful local and global explanations at no extra modeling cost, that it passes an AOPC faithfulness test (a 2.4x improvement over random ablation), and that the stability of attributions across time windows provides a practical concept-drift signal. SGFA thus demonstrates that explainability, far from being a tax on accuracy or latency, can actively improve a cloud-native fraud-scoring pipeline.

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References

[1] Jamshidi, P., Pahl, C., Mendonca, N. C., Lewis, J., & Tilkov, S. (2018). Microservices: The journey so far and challenges ahead. IEEE Software, 35(3), 24-35. https://doi.org/10.1109/MS.2018.2141039

[2] Zi, B. (2024). Cloud-native distributed systems for real-time payment intelligence. AI and Data Science Journal, 1(1), 51-56.

[3] Dean, J., & Barroso, L. A. (2013). The tail at scale. Communications of the ACM, 56(2), 74-80. https://doi.org/10.1145/2408776.2408794

[4] Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602-613. https://doi.org/10.1016/j.dss.2010.08.008

[5] Dal Pozzolo, A., Caelen, O., Le Borgne, Y.-A., Waterschoot, S., & Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41(10), 4915-4928. https://doi.org/10.1016/j.eswa.2014.02.026

[6] Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a "right to explanation." AI Magazine, 38(3), 50-57. https://doi.org/10.1609/aimag.v38i3.2741

[7] Psychoula, I., Gutmann, A., Mainali, P., Lee, S. H., Dunphy, P., & Petitcolas, F. (2021). Explainable machine learning for fraud detection. Computer, 54(10), 49-59. https://doi.org/10.1109/MC.2021.3082965

[8] Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NeurIPS) (pp. 4765-4774).

[9] Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56-67. https://doi.org/10.1038/s42256-019-0138-9

[10] Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., & Bontempi, G. (2018). SCARFF: A scalable framework for streaming credit card fraud detection with Spark. Information Fusion, 41, 182-194. https://doi.org/10.1016/j.inffus.2017.09.005

[11] Carcillo, F., Le Borgne, Y.-A., Caelen, O., & Bontempi, G. (2018). Streaming active learning strategies for real-life credit card fraud detection: Assessment and visualization. International Journal of Data Science and Analytics, 5(4), 285-300. https://doi.org/10.1007/s41060-017-0076-9

[12] Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oble, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557, 317-331. https://doi.org/10.1016/j.ins.2019.05.042

[13] Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., & Young, M. (2015). Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems 28 (NeurIPS) (pp. 2503-2511).

[14] Dal Pozzolo, A., Caelen, O., Johnson, R. A., & Bontempi, G. (2015). Calibrating probability with undersampling for unbalanced classification. In IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 159-166). IEEE. https://doi.org/10.1109/SSCI.2015.33

[15] Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2018). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784-3797. https://doi.org/10.1109/TNNLS.2017.2736643

[16] Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning (ICML) (pp. 233-240). ACM. https://doi.org/10.1145/1143844.1143874

[17] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357. https://doi.org/10.1613/jair.953

[18] Bahnsen, A. C., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134-142. https://doi.org/10.1016/j.eswa.2015.12.030

[19] Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234-245. https://doi.org/10.1016/j.eswa.2018.01.037

[20] Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448-455. https://doi.org/10.1016/j.ins.2018.02.030

[21] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324

[22] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems 30 (NeurIPS) (pp. 3146-3154).

[23] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (pp. 785-794). ACM. https://doi.org/10.1145/2939672.2939785

[24] Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT) (pp. 177-186). Springer. https://doi.org/10.1007/978-3-7908-2604-3_16

[25] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

[26] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv, arXiv:1702.08608. https://doi.org/10.48550/arXiv.1702.08608

[27] Teng, D., Rhee, M., Qin, Y., Zi, B., & Liu, W. (2026). SW-SpeedDLM: Sliding-window speculative decoding for diffusion language models under long-context constraints. Mathematics, 14(12), Article 2137. https://doi.org/10.3390/math14122137

[28] Wang, Z., Yang, J. S., Shang, W., & Ding, J. (2026). FairPromote: Explainable and fairness-aware talent promotion prediction via adversarial debiasing and SHAP-based interpretation. IEEE Access, 14, 1-16.

[29] Teng, D. (2025). TEAS: Token- and energy-aware autoscaling for cost-efficient LLM serving. AI and Data Science Journal, 6(3), 1-10.

[30] Zhang, F., Guo, Z., Ding, J., Yang, J., & Liu, W. (2026). Adaptive sensor fusion for robust perception in dense fog: A gated vision and LiDAR integration framework. Sensors, 26(12), Article 3728. https://doi.org/10.3390/s26123728

[31] Zi, B. (2024). Large language models for enterprise workflow automation in financial operations. Innovation and Technology Studies, 1(1), 24-29.

[32] Ding, J., Shen, Z., & Liu, W. (2026). Game-theoretic cost-sensitive adversarial training for robust cloud intrusion detection against GAN-based evasion attacks. Applied Sciences, 16(8), Article 3944. https://doi.org/10.3390/app16083944

[33] Wang, B., Wang, Z., Zhao, W., Zhang, F., & Shang, W. (2026). DRL-Adapt: Deep reinforcement learning for adaptive routing convergence optimization in large-scale networks. IEEE Open Journal of the Computer Society, 7, 1-14.

[34] Teng, D. (2025). PACO: Predictive auto-configuration for SLO-constrained large language model inference serving. Innovation and Technology Studies, 2(1), 1-10.

[35] Jiao, Y., Fan, H., Yue, X., Ping, W., Sun, T., & Wang, J. (2026). Dynamic heterogeneous graph contrastive learning for uncovering collusive financial fraud. Scientific Reports, 16, Article 14567. https://doi.org/10.1038/s41598-026-58938-5

[36] Ping, W., Jiao, Y., Fan, H., & Zhang, X. (2026). Multimodal fraud detection in financial statements: A trimodal attention network with contrastive evidence chain construction. IEEE Access, 14, 1-17.

[37] Liang, Y., Jiao, Y., Ping, W., Fan, H., & Han, X. (2026). Adaptive event-driven labeling: A neuro-symbolic multiagent framework for causal inference in non-stationary time series. IEEE Access, 14, 1-18.