Explainable Fraud Analytics in Online Payments

Author(s)	Ms. Grishma Manohar Bansod
Country	India
Abstract	The rapid growth of digital payment systems has significantly increased the risk of fraudulent activities such as identity theft, account takeover, and unauthorized transactions. Conventional fraud detection models based on machine learning often act as black-box systems, providing high accuracy but limited interpretability. This lack of transparency reduces user trust and creates challenges for regulatory compliance. This paper proposes an Explainable Fraud Analytics framework for online payments that integrates machine learning models with Explainable Artificial Intelligence (XAI) techniques. The proposed approach combines robust fraud detection algorithms with interpretability methods such as SHAP, LIME, and counterfactual explanations to provide human-understandable insights for each fraud decision. The system aims to enhance fraud detection accuracy while reducing false positives and ensuring transparency, accountability, and regulatory compliance. The proposed framework contributes to the development of trustworthy and interpretable fraud detection systems suitable for modern digital payment ecosystems.
Keywords	Fraud Detection, Online Payments, Explainable AI, Machine Learning, XAI, SHAP, LIME, Digital Transactions, Model Interpretability
Field	Engineering
Published In	Volume 7, Issue 1, January-February 2026
Published On	2026-02-06

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