FinGuard:
AI Loan Eligibility & Risk Assessment
An ML-powered credit risk platform using alternative data sources — cash flow, rent history, employment signals — to expand lending access for the 45 million Americans who are credit invisible, without increasing lender default rates.
The Problem
45 million Americans are "credit invisible" — no credit score, or a score too thin to qualify for traditional loans. This isn't because they're high-risk. It's because the data the system uses doesn't capture their actual financial behavior. Legacy credit models look backward at credit utilization and account age, missing the signal available in how people actually manage money today.
Lenders want to grow loan books but can't increase default risk. Traditional models have optimized as far as they can. Alternative data — available right now via Open Banking APIs — contains better predictive signals. But nobody has built a clean, compliant, explainable product to expose it to lenders.
Credit risk is better predicted by behavioral patterns, cash flow, and alternative data signals than by backward-looking credit scores. FinGuard exposes these signals in a compliant, explainable, API-first product.
User Personas
3 years of on-time rent, healthy cash flows, no US credit history. FICO: N/A. Banks auto-reject. Dealership offers 22% APR.
"I earn good money and pay everything on time. Why won't anyone lend to me?"
Board wants default rates <2% but also wants loan volume growth. No in-house data science team. Current model leaving creditworthy applicants untouched.
"I know some of the people we reject would pay us back perfectly. I just can't prove it."
Alternative Data Sources — The Core Differentiator
Traditional models use: payment history, credit utilization, account age, credit mix, new inquiries. FinGuard uses all of that plus:
| Data Source | Signal | How Accessed |
|---|---|---|
| Bank Account Cash Flow | Income stability, spending patterns, savings behavior | Open Banking APIs (Plaid, MX) |
| Rent Payment History | On-time behavior for largest monthly expense | Experian RentBureau, rental providers |
| Utility & Telecom Payments | Long-term payment consistency | Data partnerships, user permission |
| Employment Verification | Job tenure, income trajectory | Argyle, Work Number API |
| Gig Income Signals | Income regularity for non-traditional earners | Stripe, PayPal, Venmo patterns |
Core Features & RICE Prioritization
| Feature | Reach | Impact | Confidence | Effort | Score |
|---|---|---|---|---|---|
| Explainability Layer (SHAP/LIME) | 4 | 5 | 4 | 3 | 26.7 |
| Risk Scoring Engine | 5 | 5 | 4 | 4 | 25.0 |
| API Gateway | 3 | 4 | 3 | 2 | 18.0 |
| Cash Flow Analysis Module | 4 | 4 | 3 | 3 | 16.0 |
| Lender Dashboard | 3 | 4 | 4 | 3 | 16.0 |
Every decision must produce a human-readable explanation: top 3 positive factors, top 3 negative factors, what would change the decision, and an FCRA-compliant adverse action notice. This isn't a nice-to-have — it's legally required, and the #1 reason lenders can trust the system.
Compliance Architecture
Regular disparate impact testing across protected characteristics. Fairness constraints baked into model training. Third-party bias audit before launch. This is the most important technical requirement in the PRD.
- FCRA: Adverse action notice generation; user right to dispute every decision
- ECOA: No protected class data used; equal credit opportunity monitoring
- SOC 2 Type II: Full audit trail for all data access and decisions
Key Risks
| Risk | Impact | Mitigation |
|---|---|---|
| Model perpetuates historical lending bias | Critical | Regular disparate impact testing; fairness constraints; third-party audit before launch |
| Open Banking access restrictions | High | Multi-provider redundancy; graceful fallback to traditional data only |
| Regulatory reclassification as credit reporting agency | High | Legal review in each market; FCRA compliance from day one |
Success Metrics
Revenue Model
| Stream | Model | Price |
|---|---|---|
| API Calls | Per-decision pricing | $0.50–$2.00/decision (volume-based) |
| Lender SaaS | Monthly subscription | $2,000–$15,000/month |
| Referral / Lead Gen | Loan brokerage | 1–3% of funded loan amount |