Financial Services
Enterprise Client (Anonymized) — Mid-Market Lending
Cutting credit decisioning from five days to under two
68% faster credit decisioning
Deployment · 14 weeks
The situation
A mid-market commercial lender with 140 underwriters was losing deals to faster competitors. The underwriting cycle averaged 5.2 days, most of it spent manually pulling data out of financial statements, tax returns, and bank records submitted in inconsistent formats. Loan officers were frustrated; borrowers were walking.
Two prior attempts to buy an off-the-shelf document AI product had stalled in procurement and integration — neither vendor would embed with the underwriting team to make the system fit the actual workflow.
The baseline
- Average underwriting cycle: 5.2 days
- Files requiring rework due to missing or misread data: 22%
- Underwriter throughput: 3.1 files per week
- SLA compliance (decision within contractual window): 74%
The deployment
Three engineers embedded with the underwriting and credit risk teams for fourteen weeks. We built a document extraction pipeline that reads financial statements, tax filings, and bank statements regardless of source format, and a decision-support layer that flags the specific risk factors an underwriter needs to review — not a black-box approval, a structured brief.
The system integrates directly with the existing loan origination system. Every extracted field is traceable to the source document and page, and every risk flag cites the underwriting policy clause that triggered it, because credit risk sign-off on auditability was a condition of go-live, not an afterthought.
The outcome
- Underwriting cycle: 5.2 days → 1.7 days (68% faster)
- Rework rate: 22% → 6%
- Underwriter throughput: 3.1 → 4.8 files per week (55% increase)
- SLA compliance: 74% → 97%
The lender closed the following quarter with a measurably higher win rate on time-sensitive deals, though that number sits outside what we measure directly — we hold ourselves to the underwriting metrics above.
What the client owns now
- The document extraction pipeline and its source-to-field traceability layer
- The risk-flagging logic and its policy-citation mapping, versioned alongside credit policy
- The audit trail system credit risk uses for quarterly reviews
- A runbook for onboarding new document types without CopperPin involvement
Client identity, sector specifics, and select figures in this case study have been altered or composited to protect client confidentiality, standard practice for published engagement work. The deployment, methodology, and results described reflect a real CopperPin engagement.