AI Document Verification for Digital Lending
How we would approach multilingual loan-document verification for an Indian NBFC under data-localisation rules.
An NBFC underwriting SME loans verifies many document types per application by hand — PAN, GST returns, bank statements, ITRs — from scans and phone photos in several languages. This scenario walks through an explainable verification pipeline built for RBI audit expectations.
What we would agree up front
All processing within Indian data centres
Incomplete applications caught at submission, not at final review
An explainable, auditable record for every automated check
A composite scenario showing how we approach this kind of problem, not a specific client engagement.
The situation
Underwriting takes days, many applications are rejected late for incomplete documentation, and documents arrive as blurry photos as often as clean PDFs, in English and regional languages.
Constraints we would design around
Data must stay in India to meet RBI data-localisation requirements. Every automated decision must be explainable to an auditor. The pipeline must integrate with an existing loan management system.
Approach
Start with a discovery sprint shadowing the underwriting team, because the real workflow always has undocumented checks. Phase 1 covers the highest-volume document types. Every extracted field carries a confidence score and a source reference, and cross-checks run against verification APIs (PAN, GSTIN) where available.
Architecture
Hosted in an Indian cloud region. FastAPI processing pipeline, OCR plus vision and multilingual LLM extraction, PostgreSQL for the audit trail. An underwriter workspace shows extracted fields beside the original document, and a REST adapter connects to the loan management system.
Design notes
Multilingual support has to be designed in from day one, not added later. Explainability — showing exactly why a field was flagged — is what underwriting and compliance teams end up valuing most.