Technical deep-dives on UPI fraud detection, RBI compliance, machine learning for payment security, and fraud operations in India's digital payment ecosystem.
UPI fraud losses crossed ₹3,800 crore in FY26. Banks face RBI penalties, brand damage, and rising churn without real-time detection. Here's the full cost breakdown — and what genuine real-time detection requires.
A practical 20-point checklist covering real-time monitoring, CERT-In incident reporting, FMR filing, mule detection obligations, audit trail requirements, and SAR/STR filing — with mandatory vs. recommended tags.
Cuts through vendor marketing to give banks, fintechs, and NBFCs 8 concrete evaluation criteria — with specific demo questions, red flags to watch for, and the gap between "real-time" claims and reality.
A compliance breakdown of RBI's latest Master Direction on fraud risk management — covering real-time monitoring, mule account detection obligations, and CERT-In incident reporting timelines.
How SHAP (SHapley Additive exPlanations) values make XGBoost fraud models interpretable for RBI auditors — with code examples using the vcurd ML pipeline.
Data-driven analysis of the most prevalent UPI fraud attack vectors in FY26 — social engineering surges, OTP interception, mule network clusters, and what's changed since FY25.
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