Document AI speeds onboarding 80%
An AI document pipeline that extracts and validates data to accelerate customer onboarding.
- 80%
- faster onboarding
- 50%
- less manual review
- 99%
- extraction accuracy
The problem we solved
Manual document review created days-long onboarding delays and a poor customer experience.
What the client was doing before
Loan applicants uploaded ID documents, payslips and bank statements through a web portal. A compliance team member downloaded each file, opened it, extracted the relevant data by hand into a spreadsheet, cross-referenced it against validation rules and either approved or flagged the application. Processing a single application took 45–90 minutes. A backlog of 3–5 days was normal.
How we solved it
We built an AI pipeline to extract, validate and route document data with human review only for exceptions.
How the system is built
The key components and how they connect.
- Upload portal
- AI extraction
- Validation rules
- Review queue
- Core system
Technologies used
What we delivered
- Onboarding time cut by 80%
- Manual review halved
- Audit-ready records for every application
ROI
Faster onboarding directly increased approved-loan throughput and customer satisfaction.
What we learned from this engagement
Practical takeaways that inform how we approach similar projects.
- Document extraction accuracy is not uniform — handwritten documents and low-resolution scans require a separate pre-processing step before LLM extraction.
- Compliance teams need to own the validation rules — engineers should not define what counts as a valid payslip.
- Audit logging from day one is not optional in financial services — it is a prerequisite for regulatory sign-off.
"Onboarding went from days to hours. Our team reviews exceptions instead of every single page."
Head of Risk
Fintech Lender
Service
AI EngineeringIndustry
FinanceWant results like this?
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