Automated Cheque and KYC Document Fraud Detection Using Computer Vision Techniques

Objective: To develop and validate a computer vision-based solution for detecting fraudulent cheques and KYC documents in Indian banking operations.

Cheque and KYC document fraud remains a persistent challenge for Indian banks, leading to significant financial losses, operational delays, and compliance risks. Manual verification processes are slow, prone to human error, and often ineffective against sophisticated forgeries, especially in high-volume urban and semi-urban branches.

The project involves gathering real or publicly available images of Indian cheques and KYC documents (e.g., Aadhaar, PAN cards), identifying common fraud patterns (e.g., image tampering, signature forgery, mismatched photo IDs), and designing a computer vision pipeline using machine learning and deep learning techniques to automate fraud detection.

Key deliverables include: a labeled dataset of genuine and fake cheque/KYC images, a trained computer vision model, detailed accuracy and performance metrics, a prototype user interface for bank staff, and a business analysis of false positives/negatives and operational impact.

This solution will help Indian banks reduce manual workload, improve fraud detection rates, and support compliance audits, directly informing investment decisions in automated verification technologies and policy updates.