Overview
Private lenders operate on tight timelines. Every day a closing package is delayed, a term sheet sits in someone’s inbox, or a credit exception goes unnoticed, the entire deal cycle slows down. For this lender, rising deal volume meant analysts were spending hours reviewing multi-document closing packages, drafting term sheets manually, and validating underwriting models against credit policy. These steps were essential but increasingly unsustainable.
The lender partnered with StackAI to deploy a suite of AI agents built around applied OCR, multi-LLM reasoning, and automated document analysis. Within six weeks, the lending team reduced document processing time by an estimated 70–80% and saved 25–30 analyst hours per deal. Across 400–500 deals per year, this represents more than 10,000 hours recovered annually. The results?
Reduced document processing time by 70–80% within six weeks
Saved 25–30 analyst hours per deal
Handled 400–500 deals per year
Recovered 10,000+ analyst hours annually
The lender now moves deals from underwriting to close with far less friction, even as volume continues to climb.
Closing Package Checks, Automatically
The Problem: Manual Closing Packages Slow Down Every Deal
Closing packages arrive as large, mixed-format sets of PDFs, scans, attachments, and borrower uploads. Analysts were spending 25–40 minutes per deal manually opening each file, checking for missing or expired documents, organizing inconsistencies, and stitching everything into a usable checklist.
This effort compounded quickly. With 150–200 closings per month, the team was spending more than 80 analyst hours each week simply verifying whether required documents were present, missing, or out of compliance.
Errors slipped through, turnarounds were unpredictable, and closing delays became common during peak months.
The Solution: Applied OCR That Flags Issues Instantly
The lender deployed an AI agent that processes the entire closing package in one pass using OCR, classification logic, and a structured rules engine. The agent:
Extracts and reads the content of every document, including low-quality scans
Classifies each file as present, missing, or expired
Generates a plain-text checklist following the lender’s format
Inserts a subject line, due dates, loan number, and borrower greeting
Outputs a full summary that can be sent directly to the borrower or closing attorney
A closing package review that previously took 30 minutes now completes in 12–15 seconds. Across monthly volume, this saves roughly 60–80 hours per week and dramatically reduces missed items that trigger last-minute scrambling.
Credit Policy Assessment, Powered by AI
The Problem: Credit Policy Compliance Required Painstaking Cross-Checks
Before any deal advanced to committee, analysts needed to check the underwriting model and submission files against credit standards: maximum LTV, minimum DSCR, minimum borrower experience, market restrictions, and loan size parameters.
This process required switching between a PDF submission, Excel model, credit policy documentation, and internal notes. A single review could take 25–30 minutes—and even longer when multiple exceptions had to be documented.
The Solution: Instant Pass/Fail Checks With Auto-Drafted Exception Memos
The Credit Policy Checker agent evaluates every submission against the policy rules using OCR and Excel parsing. It then:
1. Reads the underwriting PDF or model
2. Determines Pass/Fail for LTV, DSCR, Experience, Market, and Loan Size
3. Provides plain-language explanations
4. Automatically drafts a credit exception memo preview if any factor fails, creates a Word document, and emails it to the reviewer
The entire analysis completes in minutes, reducing a hour-long review to nearly zero. Analysts now focus solely on judgment and escalation rather than mechanical policy checks, and the lender estimates that this single agent saves more than 4,000 hours annually.
Conclusion: Faster Decisions, Fewer Errors, and Scalable Deal Volume
By integrating AI agents across underwriting and closing operations, this private lender transformed its deal process end-to-end:
• 70–80% reduction in manual document handling
• 25–30 hours saved per deal cycle
• More than 10,000 annual hours recovered
• Faster term sheets and fewer closing delays
• Stronger credit governance with automated exception flagging
The result is a lending operation that scales without adding headcount and delivers more predictable, compliant, and timely decisions for borrowers and capital partners.
Want to see how StackAI can help your enterprise save thousands of hours per year? Get a demo here.
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