Blueprint · Financial Services
Loan Document Processing Agent for a Mid-Size Lender
How we would automate loan file intake, cross-checks and exception routing with analysts staying in control.
Illustrative scenario. This shows how we would approach the problem. It does not describe a specific client, and the numbers are targets to validate in a pilot.
The situation
A mid-size lender receives loan files as mixed PDFs and scans. Analysts retype fields into the loan system, then manually cross-check income, identity and bank statements. Turnaround depends on queue length, and errors surface late.
Our approach
- Classify every incoming document and split multi-document scans
- Extract fields with a confidence score on each value
- Cross-check values across documents with deterministic rules
- Send low-confidence items to an analyst queue with the source highlighted
- Log every step for audit
Typical stack
- Vision-capable LLM for extraction
- Python rule engine
- Temporal for orchestration
- PostgreSQL audit store
- Analyst review UI
What we would measure
| Metric | Target approach |
|---|---|
| Analyst minutes per file | Reduce by half or more |
| Field-level accuracy | Agree threshold in pilot, typically 97%+ after review |
| Share of files needing no rework | Baseline, then improve monthly |
The lesson
The rule engine does as much work as the model. LLMs read; deterministic code verifies. That split is what makes this auditable.
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