Automated Multi-Document Verification and Fraud Detection Platform for Welfare Schemes

Objective: To develop a digital platform that automates document verification and detects fraudulent enrolments for government welfare scheme beneficiaries.

India's social welfare schemes face significant leakages and delays due to manual and inconsistent document verification, resulting in exclusion of genuine beneficiaries and fraudulent claims. Departments such as Rural Development and Social Justice rely on various documents—income, address, caste, and identity proofs—which are often forged or recycled, undermining SDG 16 targets for accountable institutions.

This project proposes an AI-powered online system that ingests applicant-submitted documents, cross-verifies them against trusted public datasets (e.g., electoral rolls, land records, ration card databases), and flags inconsistencies or likely fraud for officer review, all without requiring Aadhaar APIs. The platform will use computer vision for document authenticity checks, NLP for data extraction, and rule-based matching for cross-database validation.

Key features include: secure document upload; OCR and tampering detection; entity extraction; cross-verification against multiple open government datasets; a fraud-risk scoring dashboard; and a workflow for officers to review and approve/reject flagged applications. The working prototype will support at least two major schemes (e.g., pension, PDS) and demonstrate real-state datasets integration.

Measurable impact will be seen through reduced manual workload, lower fraud rates, and faster enrolment turnaround in pilot blocks. The solution is designed to scale across departments and integrate new datasets, enhancing transparency and trust in e-governance processes at scale.