Explainable AI Pipelines for Behavioral Fraud Modeling in Online Retail Environments

Siqi Chen

doi:10.71222/n9rch013

Authors

Siqi Chen Columbia University, New York, NY, USA Author

DOI:

https://doi.org/10.71222/n9rch013

Keywords:

explainable AI, behavioral fraud modeling, logistic regression ensembles, SHAP interpretability, e-commerce cybersecurity, false positive reduction, predictive transaction scoring, scalable ML deployment

Abstract

With online retail fraud posing growing threat to consumers, explainable AI (XAI) has become increasingly important for transparent and actionable risk assessment. This paper presents an XAI-integrated pipeline for behavioral fraud modeling, fusing supervised ensembles of logistic regression and random forests with unsupervised isolation forests to detect both known and emerging behavioral anomalies, including irregular cart sequences and geolocation inconsistencies. SHAP-based attributions are incorporated to deliver instance-level explanations that enhance auditability and support compliance requirements (e.g., PCI DSS). Using a heterogeneous dataset of 150,000 transaction records, the proposed system achieves an F1-score of 0.93 and reduces false positives and manual interventions by 82% relative to an industry-standard rule-based baseline. The architecture supports offline batch analysis and scalable serverless deployment. Pilot studies indicate potential operational cost reductions driven by decreased review workloads and improved detection efficiency. The open-source implementation fosters iterative community refinements, advocating XAI's role in fortifying e-commerce resilience against evolving threats like synthetic identities.

References

1. A. Srivastava, K. D. Singh, and V. Kumar, "E-commerce fraud detection: A systematic review of current trends, challenges, and opportunities," Journal of Financial Crime, vol. 31, no. 2, pp. 345-367, 2024.

2. A. I. Trustworthy, "Explainability in fraud detection: Trustworthy AI and pattern detection," In Proceedings of the 1st IFIP WG 12.13 International Conference on Artificial Intelligence for Global Security (AI4GS), 2024, pp. 178-192.

3. D. Cirqueira, M. Helfert, and M. Bezbradica, "Towards design principles for user-centric explainable AI in fraud detection," In Proceedings of the International Conference on Human-Computer Interaction, 2021, pp. 21-40. doi: 10.1007/978-3-030-77772-2_2

4. T. Awosika, R. M. Shukla, and B. Pranggono, "Transparency and privacy: The role of explainable AI and federated learning in financial fraud detection," IEEE Access, vol. 12, pp. 64551-64560, 2024. doi: 10.1109/access.2024.3394528

5. R. Kapale, P. Deshpande, S. Shukla, S. Kediya, Y. Pethe, and S. Metre, "Explainable AI for fraud detection: Enhancing transparency and trust in financial decision making," In Proceedings of the 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education, and Industry (IDICAIEI), 2024, pp. 1-6.

6. E. R. Mill, W. Garn, N. F. Ryman-Tubb, and C. Turner, "Opportunities in real-time fraud detection: An explainable artificial intelligence (XAI) research agenda," International Journal of Advanced Computer Science and Applications, vol. 14, no. 5, pp. 1172-1186, 2023.

7. S. N. Nobel, S. Sultana, S. P. Singha, S. Chaki, M. J. N. Mahi, and T. Jan, "Unmasking banking fraud: Unleashing the power of machine learning and explainable AI (XAI) on imbalanced data," Information, vol. 15, no. 6, p. 298, 2024.

8. D. Vijayanand, and G. S. Smrithy, "Explainable AI-enhanced ensemble learning for financial fraud detection in mobile money transactions," Intelligent Decision Technologies, vol. 19, no. 1, pp. 52-67, 2025.

9. W. Min, W. Liang, H. Yin, Z. Wang, M. Li, and A. Lal, "Explainable deep behavioral sequence clustering for transaction fraud detection," arXiv preprint, 2021.

10. A. Bhowmik, M. Sannigrahi, D. Chowdhury, A. D. Dwivedi, and R. R. Mukkamala, "Dbnex: Deep belief network and explainable AI-based financial fraud detection," In Proceedings of the IEEE International Conference on Big Data, 2022, pp. 3033-3042.

11. Y. Vivek, V. Ravi, A. Mane, and L. R. Naidu, "Explainable artificial intelligence and causal inference-based ATM fraud detection," In Proceedings of the IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr), 2024, pp. 1-7.

12. S. M. Lundberg, and S. I. Lee, "A unified approach to interpreting model predictions," In Proceedings of Advances in Neural Information Processing Systems, 2017, pp. 4768-4777.

13. P. Fukas, J. Rebstadt, L. Menzel, and O. Thomas, "Towards explainable artificial intelligence in financial fraud detection: Using Shapley additive explanations to explore feature importance," In Proceedings of the International Conference on Advanced Information Systems Engineering, 2022, pp. 109-126. doi: 10.1007/978-3-031-07472-1_7

14. A. B. Arrieta, N. Díaz Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, and A. Barbado, "Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," Information Fusion, vol. 58, pp. 82-115, 2020.