FRAUD ALERT • Deepfake KYC attacks rising 340% YoY • RBI AI fraud compliance tightening • Synthetic voice fraud increasing • Behavioral AI becoming mandatory • UPI mule-account detection accelerating • AI TRiSM adoption expanding across BFSI
Module 03

🛡️ Agentic AI Fraud Defense

Deepfake KYC vulnerability assessment, Video-KYC attack vector analysis, and real-time transaction anomaly detection powered by behavioral AI.

Deepfake Growth YoY
340%
KYC False Negative Rate
18%
UPI Fraud FY26
₹8.7KCr
FinWithDip Detection Rate
94%

Video-KYC Vulnerability Map

Attack vector analysis across the KYC pipeline — aligned with RBI Master Direction on KYC (2023).

Transaction Anomaly Monitor

Simulate a transaction to test behavioral AI fraud detection.

AI TRiSM Framework

Multi-modal biometric fusion — combining facial geometry, micro-expression analysis, and behavioral keystroke dynamics — reduces Deepfake KYC bypass rate from 18% to <2%. NIST AI RMF Govern 1.1 compliant.

AI Fraud Threat Landscape — India 2026

Deepfake Face Injection
GAN-generated face overlaid on live Video-KYC stream via OBS pipeline. Bypasses passive liveness detection in 18% of cases.
Detection Coverage82%
AI Document Forgery
LLM-generated document metadata that passes OCR verification but fails forensic analysis. Aadhaar XML manipulation in 12% of cases.
Detection Coverage66%
Session Hijacking (WebRTC)
MITM interception of WebRTC video stream enabling real-time face substitution without KYC agent awareness.
Detection Coverage62%
Synthetic Voice Cloning
AI voice cloning for phone-based KYC. Current spectral analysis detects 71% of synthetic voices — leaving a 29% gap.
Detection Coverage71%
Executive Abstract · AI TRiSM

The convergence of Generative AI with financial crime creates an unprecedented threat surface for India's digital banking ecosystem. Deepfake-enabled fraud has grown 340% YoY, with Video-KYC representing the most critical vulnerability: current liveness detection models exhibit an 18% false-negative rate against GAN-generated face injections — a gap that sophisticated criminal networks are actively exploiting at scale.

FinWithDip's Agentic AI Fraud Defense framework is built on the NIST AI Risk Management Framework (AI RMF) and Gartner's AI TRiSM (Trust, Risk, and Security Management) model. The architecture layers behavioral biometrics, transaction graph analysis, and multi-modal liveness verification to reduce the attack surface across the entire customer lifecycle — from Video-KYC onboarding to real-time UPI transaction monitoring.

For Indian banks, the regulatory imperative is clear: RBI's circular on Digital Payment Security Controls (2024) mandates AI-powered fraud detection for institutions processing >₹1KCr monthly. Non-compliance carries both direct financial penalties and reputational CASA attrition, estimated at ₹180Cr per major fraud event. AI TRiSM adoption is a non-negotiable element of the modern BFSI risk architecture.