AI-Based Fraud Detection in Digital Payment Systems

Abstract: Digital payment systems such as Unified Payments Interface (UPI), mobile wallets, internet banking and card-based payments have made transactions faster and more convenient. At the same time, digital transactions can be targeted by card-not-present fraud, account takeover, phishing, social engineering, suspicious beneficiary activity and other forms of payment abuse. Traditional rule-based systems are useful for identifying known patterns, but fixed rules may struggle when fraud strategies change or when legitimate customer behaviour varies. This paper proposes an AI- based fraud detection framework that combines transaction-risk indicators, machine-learning classification and rule- based checks to identify potentially fraudulent digital payments. The framework considers features such as transaction amount, frequency, time, device or session changes, beneficiary history and deviations from a customer’s usual activity, subject to lawful access and privacy safeguards. A risk-scoring component classifies transactions as low, medium or high risk and recommends approval, additional verification or temporary review. The paper presents a conceptual system design and assessment methodology rather than claiming results from an experiment. Its practical value should be evaluated using representative, appropriately protected transaction data and metrics such as precision, recall, F1-score, false-positive rate and detection latency. The proposed approach aims to support timely fraud prevention while preserving customer convenience, explainability and responsible handling of financial data.

Keywords: Artificial Intelligence (AI), Digital Payments, Fraud Detection, Machine Learning, UPI, Anomaly Detection, Transaction Risk Scoring, Financial Cybersecurity, Data Privacy.