Development of UPI Transaction Fraud Detection and Risk Scoring Engine

Objective: To design and implement an automated engine for detecting fraudulent UPI transactions and assigning real-time risk scores to users.

India’s Unified Payments Interface (UPI) has revolutionized digital payments, but its rapid adoption has also led to a surge in transaction fraud, phishing, and unauthorized transfers. The challenge is to build a robust system that can proactively identify suspicious activities and minimize financial losses for banks and customers.

The project team will conduct domain research on fraud patterns in UPI, collect anonymised transaction data (from NPCI, RBI reports, or simulated datasets), and analyze behavioral indicators such as frequency, amount, device ID, and geo-location. Machine learning models and rule-based heuristics will be designed to classify transactions and score user risk.

Deliverables include a working prototype of the detection engine, a detailed risk-scoring algorithm, analysis of fraud trends, and a dashboard for stakeholders. The team will validate accuracy using test datasets and recommend operational strategies for banks and fintechs.

Business impact: The solution enables banks and UPI service providers to make real-time decisions on transaction blocking, customer alerts, and compliance, ultimately reducing fraud losses and enhancing trust in India’s digital payment ecosystem.