The Company
FinanceFlow (name anonymized) is a digital lender specializing in small business loans and personal mortgages. Founded in 2018, they grew rapidly during the pandemic as businesses sought fast, online-first lending solutions.
By early 2024, FinanceFlow was processing over 10,000 loan applications per month. But their operational costs were unsustainable.
The Problem
Each loan application required manual review of 8-15 documents: pay stubs, tax returns, bank statements, employment verification letters, and more.
- • 22 full-time underwriters dedicated to document data entry
- • 45 minutes average processing time per application
- • ~12% error rate in manual data entry
- • 3-5 day turnaround time from submission to decision
"We were growing fast, but we couldn't hire underwriters fast enough. And even if we could, the manual data entry was killing our margins. We needed automation, but every OCR tool we tried was too unreliable for production."— David Park, COO of FinanceFlow
The Search for a Solution
FinanceFlow's tech team evaluated several document processing vendors:
Implementation
FinanceFlow's engineering team integrated Retriv.ai in just 3 weeks. Here's how they did it:
Week 1: Schema Design
Defined extraction schemas for each document type (pay stubs, W-2s, 1099s, bank statements, etc.). Retriv's team provided consultation on best practices.
{
"employer_name": "string",
"employee_name": "string",
"gross_pay": "float",
"ytd_earnings": "float",
"pay_period_start": "date",
"pay_period_end": "date"
}Week 2: API Integration
Built a microservice that receives uploaded documents, calls Retriv's Extract API, and pushes structured data into their Encompass LOS (Loan Origination System).
Week 3: Human-in-the-Loop Review
Built a UI for underwriters to review and correct extracted data before final submission. Used Retriv's visual grounding to show exactly where each field was extracted from.
Results
FinanceFlow launched their automated document processing in April 2024. The results exceeded expectations:
"Retriv.ai didn't just save us money — it fundamentally changed our business model. We can now compete with the big banks on turnaround time while maintaining better margins. Our NPS score from borrowers went up 18 points because we're so much faster."— David Park, COO of FinanceFlow
What's Next
FinanceFlow is now expanding their use of Retriv.ai:
- Processing commercial loan applications (more complex documents)
- Building automated fraud detection using Retriv's metadata analysis
- Extracting data from title reports and appraisals for mortgage processing
- Creating a RAG-powered chatbot for loan officers to query historical applications
Key Takeaways
- API-first beats custom models for most lending use cases
- Human-in-the-loop workflows balance automation with accuracy
- Visual grounding is critical for building user trust
- 3-week implementation is realistic for mid-sized lenders
Want Similar Results?
Schedule a demo to see how Retriv.ai can transform your document processing.