Loan Origination OS | Finance Automation

Back to Case Studies
Finance

Loan Origination & Underwriting OS

End-to-end loan processing automation from application intake through underwriting and closing.

Finance automation case study featuring document processing and workflow automation.

-60%

Time to Close

Automated

Document Collection

+25%

Underwriting Accuracy

Overview

A lending company needed to streamline loan origination from application through closing. Manual document collection and underwriting were causing 3-week delays. The automated system reduced time-to-close by 60%.

Business Context

The private lending firm was experiencing rapid growth but their manual loan processing couldn't keep pace with demand. Borrowers were abandoning applications due to lengthy back-and-forth document requests via email, and the underwriting team was overwhelmed with repetitive verification tasks that consumed entire days. The firm was losing competitive deals to faster-moving lenders who could provide term sheets in 48 hours while their process took three weeks. Operational costs per loan were unsustainably high due to the labor-intensive nature of document collection and manual underwriting analysis. Leadership recognized that digital transformation of their origination process was essential for both growth and profitability, and that continuing with manual processes would ultimately limit their ability to compete in an increasingly technology-driven lending market.

How We Built It

We architected a complete loan origination system starting with a self-service borrower portal that guides applicants through document submission with real-time validation, progress tracking, and intelligent prompts that ensure complete packages from the start. The portal integrates with Plaid for instant bank account verification and income data, eliminating the need for manual bank statement uploads in many cases. The AI document extraction engine uses OpenAI's vision capabilities combined with custom extraction logic to parse pay stubs, tax returns, W-2s, bank statements, and business financials with high accuracy, handling the variety of formats that different employers and institutions produce. Extracted data flows into automated verification workflows that cross-reference income against stated amounts, validate employment through database lookups, pull credit reports automatically, and flag discrepancies for human review rather than failing silently. The underwriting decision engine implements the firm's credit policies as configurable rules, scoring each application across multiple risk dimensions and generating preliminary decisions within minutes rather than days. A sophisticated exception handling system routes edge cases to human underwriters with all relevant context pre-assembled, including the specific reasons for escalation and suggested next steps. The pipeline dashboard provides real-time visibility into every loan's status, bottlenecks, SLA compliance, and projected close dates. Integration with title companies, appraisers, and closing attorneys automates the downstream coordination that previously required dozens of phone calls, emails, and manual status tracking across multiple external parties.

Challenges

1

Manual document collection via email

2

Inconsistent underwriting decisions

3

Long time-to-close

4

No visibility into pipeline status

5

Compliance documentation scattered

What We Delivered

Self-service borrower portal

AI document extraction (pay stubs, tax returns, bank statements)

Automated credit and income verification

Underwriting decision engine with rule-based scoring

Pipeline dashboard with stage tracking

Compliance document generation

Tech Stack

Custom loan portal, OpenAI for document extraction, Make.com, Plaid, Retool, PostgreSQL

Tags

FinanceDocument ProcessingWorkflow AutomationAI ImplementationOpenAIMake.comRetoolAI Automation

Results

-60%

Time to Close

Automated

Document Collection

+25%

Underwriting Accuracy

Strategic Impact

The 60% reduction in time-to-close transformed the firm's competitive position in their market, enabling them to win deals they would have lost to faster competitors. Borrowers who previously went to other lenders for faster processing now choose this firm specifically for the streamlined experience, and word-of-mouth referrals have increased substantially. Loan officers can handle 3x the volume they managed previously, dramatically improving unit economics without proportional headcount increases and reducing cost per loan by over 40%. The consistency of automated underwriting has reduced default rates by eliminating human error, ensuring policy compliance on every loan, and applying risk assessment criteria uniformly rather than varying by which underwriter reviewed the file. The complete audit trail satisfies regulatory requirements and simplifies examinations — the most recent regulatory review was completed in half the time of previous reviews because all documentation was readily accessible. Perhaps most importantly, the firm can now scale their lending volume without the operational constraints that previously limited growth — they've expanded into three new markets and increased loan volume by 180% since implementation without adding operations staff. The data captured during processing has also enabled sophisticated portfolio analytics that inform pricing decisions, identify emerging risk patterns, and guide product development toward underserved market segments where the firm has competitive advantage.

Want Similar Results?

Let's discuss how we can transform your operations with automation and AI.

Book a Strategy Call
Related Case Studies