PayoffAgent
AI-powered loan payoff calculation automation that reduces 35-50 minute manual processes to 2-3 minutes with 95%+ accuracy.
Key Metrics
- Built In
- 7 Days
- Time Savings
- 95% (45min → 2min)
- Accuracy
- 95%+
- Cost Per Calculation
- $0.66
The Problem
Equipment lenders waste 35-50 minutes manually calculating loan payoffs. For lenders processing 20+ payoffs per month, that's 13-17 hours of wasted labor—$26,000 per year per employee.
The Manual Process
- 1.Download loan documents from lender portals (PDF, often scanned images)
- 2.Extract data manually: Loan amount, interest rate, origination date, payment schedule, prepayment penalties
- 3.Build Excel amortization schedule with complex formulas
- 4.Handle edge cases: Day count conventions (30/360 vs. Actual/365), variable rates, irregular payments, balloon payments
- 5.Calculate exact payoff for requested date, including accrued interest and penalties
- 6.Double-check everything because errors cost thousands in liability
The Hidden Costs:
- Labor: $8,200-$26,000 per employee per year
- Errors: 3-5% error rate requiring rework
- Liability: Miscalculated prepayment penalties can cost $5K-50K per error
- Delays: 24-48 hour turnaround slows deal closings
Lenders know this is inefficient, but manual Excel-based workflows have been "good enough" for decades. Until AI made automation actually feasible.
The Solution
PayoffAgent uses Claude AI to extract loan data from PDFs and automatically calculate exact payoff amounts—handling all the complexity that makes manual calculations so time-consuming.
How It Works
- 1
Upload Loan Document
Drag and drop the PDF (works with scanned documents, complex layouts, any format). Takes 10 seconds.
- 2
AI Extracts Loan Data
Claude's vision API reads the document and extracts: loan amount, interest rate, origination date, payment schedule, day count convention, prepayment penalty terms, and any special conditions. Takes 30-45 seconds with 95%+ accuracy.
- 3
Review & Confirm
User verifies extracted data (can edit if needed). This is the human-in-the-loop validation that ensures accuracy. Takes 30-60 seconds.
- 4
Calculate Payoff
System generates: exact payoff amount, full amortization schedule, breakdown of principal/interest/penalties, and downloadable PDF report. Takes 15 seconds.
What Makes It Work
- ✓Claude's vision API can actually understand complex financial documents (previous OCR tools couldn't handle loan agreements)
- ✓Custom calculation engine handles all day count conventions, prepayment penalty structures, and edge cases
- ✓Human validation step ensures accuracy while still achieving 95% time savings
- ✓Built by someone who understands lending—20+ years in equipment finance means I knew exactly which edge cases mattered
Manual Process
- Time: 35-50 minutes
- Error rate: ~5%
- Cost per payoff: $29-42 (labor)
- Scalability: Linear (more staff needed)
PayoffAgent
- Time: 2-3 minutes
- Error rate: <1%
- Cost per payoff: $0.66 (AI API)
- Scalability: Unlimited
Technical Implementation
Architecture
Frontend
- Next.js 14 with App Router
- TypeScript (strict mode)
- Tailwind CSS
- React Hook Form for validation
Backend
- Next.js API routes
- Supabase (PostgreSQL + Auth + Storage)
- Claude Sonnet 4 for PDF extraction
- Stripe for payments
Key Technical Challenges Solved
1. PDF Data Extraction
Challenge: Loan documents come in every format imaginable—scanned images, multi-column layouts, tables that aren't really tables, text that flows unpredictably.
Solution: Claude's vision API can actually understand document structure and context. I engineered a detailed prompt (800+ words) that describes exactly what to look for and how to handle edge cases. Accuracy: 95%+ without custom training.
2. Financial Calculations
Challenge: Day count conventions (30/360 vs. Actual/365), prepayment penalty structures (tiered, percentage-based, yield maintenance), irregular payments, variable rates—endless edge cases.
Solution: Built a calculation engine with separate modules for each concern. AI wrote the initial code, but I debugged extensively using real loan documents. The 30/360 calculation alone had subtle bugs that only appeared with certain date combinations.
3. Data Validation
Challenge: AI sometimes misreads dates, confuses percentages, or misses clauses. Can't just trust the extraction blindly.
Solution: Human-in-the-loop validation. User reviews extracted data before calculation. Added validation rules to flag suspicious values (e.g., interest rate >30% or loan amount $1M+ on a personal loan).
Development Timeline: 7 Days
- Day 1: Database schema and architecture (4 hours)
- Day 2: PDF processing with Claude API (8 hours)
- Day 3: Calculation engine and edge cases (10 hours)
- Day 4: User interface and authentication (7 hours)
- Day 5: Testing with real loan documents (9 hours)
- Day 6: Payment integration and deployment (5 hours)
- Day 7: Documentation and polish (6 hours)
Total: 49 active hours over 7 days
Results & Economics
45 minutes → 2-3 minutes
Claude API + infrastructure
vs. 5% with manual process
Pricing & ROI
Pay-Per-Calculation
per payoff calculation
Monthly Plan
50 calculations/month
Enterprise
High-volume + integrations
Customer ROI Example
Lender processing 20 payoffs/month:
- Manual labor cost: 13.7 hours × $50/hr = $685/month
- PayoffAgent cost: $399/month
- Monthly savings: $286
- Annual savings: $3,432
- ROI: Positive from month 1
Plus: Faster turnaround, fewer errors, reduced liability, and staff time freed for higher-value work.
Lessons Learned
1. Domain expertise is the unfair advantage
Anyone can ask AI to "build a loan payoff calculator." Very few people know which edge cases actually matter in equipment finance. That knowledge—20+ years in the industry—is what made this work.
2. AI doesn't replace expertise, it amplifies it
Claude wrote 80% of the code, but I had to fix the critical 20%: financial calculations, error handling, validation logic. The combination of AI coding + human expertise is what creates production-quality software.
3. Test with real data early and often
I collected 30+ real loan documents and ran them through the system on Day 5. Found bugs I never would have discovered with synthetic test data. Real-world complexity is always worse than you expect.
4. Modern infrastructure is a force multiplier
Supabase + Vercel means zero time on DevOps. Just build features and deploy. This is how I shipped in 7 days instead of 7 months.
5. Start narrow and go deep
I didn't try to build a "complete lending platform." I solved one painful problem (payoff calculations) exceptionally well. That's how you win with focused, efficient execution.
Ready to Automate Your Loan Payoff Process?
See PayoffAgent in action or discuss custom automation for your lending workflows.