Ai Solutions
Ai Solutions
A bank processed 50,000+ documents daily across loan applications, KYC, and compliance. 200+ staff performed manual data entry with a 3.2% error rate and 48-hour SLA.
A retail bank processed 50,000+ documents per day across loan applications, KYC onboarding, and compliance reviews. 200+ staff handled the intake, classification, and data entry manually. The error rate sat at 3.2%, the SLA was 48 hours, and staff turnover was a constant drag on quality. Compliance teams flagged growing audit risk with regulators.
We treated this as a multi-stage problem rather than a single OCR task. Classification came first (which document type, which workflow), then extraction (fields and entities), then validation (cross-checks against authoritative sources), then routing. Every correction made by a human in the validation UI was captured as training signal and fed back into the models weekly.
Document classification with a vision transformer fine-tuned on the bank's own document corpus. Field extraction via a hybrid NER + layout-aware model. Validation layer with deterministic business rules plus an LLM judge for ambiguous cases. Human-in-the-loop correction UI with active learning. All processing happens inside the bank's VPC; no document data leaves the perimeter.
26 weeks across three phases. Phase 1 (weeks 1-8): classification and extraction for the top 5 document types. Phase 2 (weeks 9-18): expansion to 25 document types plus validation layer. Phase 3 (weeks 19-26): active learning loop, compliance reporting, and decommissioning of legacy intake queues.
After 9 months: 87% reduction in manual processing effort, error rate down to 0.4%, SLA under 2 minutes for 92% of documents. The team of 200 was redeployed to exception handling and customer-facing advisory work. Regulator audit found zero material issues with the automated pipeline.
Want to discuss a similar challenge?
Start a Conversation