Where it breaks today
Manual credit analysis takes 3-5 days per application. Risk assessment inconsistent across analysts. High-volume periods create dangerous backlogs. Credit decisions take days. Volume can't scale.
The fix
We process applications, extract financials, and score risk automatically.
What we build
An agent is a stack.
Automated document processing
Applications from multiple channels. Financial documents classified and key metrics extracted with high accuracy.
- Multi-channel application intake
- Tax return and bank statement classification
- Financial metric extraction
- High-accuracy data capture
ML-based risk scoring
Risk scored using ML models. Decision recommended with supporting analysis.
- ML risk scoring models
- 30-40% auto-approval rate
- Decision with supporting analysis
- 2-4x underwriter productivity
How it works
Process. Score. Decide.
Application arrives
Multi-channel intake captures applications automatically.
AI processes documents
Financial documents classified, metrics extracted, risk scored.
Decision delivered
30-40% auto-approved. Rest recommended with supporting analysis.
What it delivers
Outcomes, in weeks.
Before → After
Underwriting time: 3-5 days
Same-day for qualifying
Before → After
Auto-approval: None
30-40% straight-through
Before → After
Underwriter capacity: Bottleneck
2-4x throughput
No lock-in
Built in your stack.
Live in 8-10 weeks. You own the system — sovereign, no vendor lock-in.
- Credit Bureaus (Experian, Equifax, TransUnion, etc.)
- Banking APIs (Plaid, MX, or your integration)
- Loan Origination Systems (Encompass, Calyx, or your platform)
- Runtime & Orchestration (Trinity by Ability AI)
Questions
Questions about credit underwriting
How does ML risk scoring work?
Models trained on historical credit performance data (defaults, payment patterns, financial ratios). AI learns what predicts good vs. risky credits specific to your lending criteria.
What documents can it process?
Tax returns (1040, 1120, 1065), bank statements, P&L, balance sheets, credit reports. OCR handles scanned and digital documents.
What results should we expect?
Lending teams typically see 2-4x underwriter productivity (more volume, same headcount) and 50-70% operational cost reduction. For 500 applications/month at $50 cost per app, that's $150K-$210K/year savings. Plus faster decisions improve conversion by 15-20%.
How long does implementation take?
8-10 weeks from kickoff to production. Week 1-3: Data integrations (credit bureaus, LOS). Week 4-6: ML model training on historical data. Week 7-10: Testing and validation.
Do we own the system?
Yes. You own the system. We build the infrastructure in your stack, hand over the keys, and you own it forever - no vendor lock-in.
Start here
Bring one workflow.
A 30-minute working call. We’ll map this workflow to an agent stack and tell you honestly whether it’s worth building.
Credit underwriting agent
3-5 days → same-day
Same-day credit decisions with ML-based risk scoring. 30-40% auto-approval for qualifying applications.