Document intelligence banks can audit.
I run the credit intelligence platform that turns bank statements, credit history, KYB filings and property appraisals into structured data lenders can act on. Event driven Go microservices with RabbitMQ and Temporal, a React and TypeScript SaaS plus back office, on self managed Kubernetes across AWS and GCP.
Documents that decide credit
A lender's decision rests on documents that were never designed to be read by machines: scanned statements from dozens of banks, credit history in fixed width layouts, appraisals with tables that break across pages.
Manual review is slow and inconsistent. Naive automation is worse: an extraction that is confidently wrong sends a bad file to a credit committee, and nobody can point to where the number came from. The requirement was never raw accuracy. It was accuracy with provenance, under supervision, at a cost per document that a lender will keep paying.
Platform, team, and the compliance posture
Keputusan yang saya pertahankan
Each one traded something away. That is the point of writing them down.
Resilient systems over heroics: circuit breakers, backpressure and graceful degradation designed in, rather than patched in after incidents.
Still shipping
The next year is about widening document coverage without widening the failure surface, and pushing more of the review loop back into the product so a reviewer only sees what actually needs a human.