Illustrative Case Study — 36% less credit-prep time without changing approval authority
- Client profile: $9B regional bank with a growing commercial lending portfolio
- Function: Commercial lending, credit analysis, and risk
- Volume: Approximately 4,000 new and recurring credit files annually
- Core challenge: Analysts were spending too much time assembling files before judgment could begin
- Engagement: 10 weeks from workflow diagnosis through controlled credit-team pilot
- Primary objective: Increase lending capacity and shorten decision cycles without changing credit policy or approval authority
- Existing banking systems — The solution worked around the bank’s loan origination system, CRM, customer records, and existing document repository rather than creating another disconnected source of truth.
- Document intelligence — Financial statements, borrower packages, tax documents, and supporting files were extracted and organized into a consistent structure, with source references preserved for analyst review.
- Credit preparation layer — A controlled language model helped prepare first-draft credit summaries, surface missing information, and retrieve relevant internal policy—reducing preparation work without making the credit decision.
- Validation rules — Financial figures and required fields were checked against source documents and predefined rules before the package moved forward.
- Human review workflow — Analysts and credit officers remained responsible for interpretation, exceptions, recommendations, and approvals.
- Performance visibility — A management view tracked preparation time, rework, missing information, decision cycle time, and adoption during the pilot.
The problem
The bank wanted to grow commercial lending, but adding relationship managers was not translating proportionally into completed credit decisions.
Credit analysts were spending hours gathering financial statements, reconciling numbers across documents, checking whether information was missing, searching internal policy, and creating the first version of credit memos.
Average preparation time was approximately 7.2 analyst hours per file.
The full decision cycle averaged roughly 9.6 days.
The immediate temptation was to automate the credit decision.
We deliberately did not.
What we found
The bottleneck occurred before senior credit judgment.
Files arrived inconsistently. Relationship managers and analysts repeated document requests. Analysts manually transferred the same information into several templates. Policy questions required searching multiple sources. Reviewers frequently sent packages back because supporting information or explanations were incomplete.
That meant highly trained credit professionals were spending too much time creating the decision package and not enough time evaluating it.
What we changed
Working with lending, credit, risk, and technology stakeholders, we redesigned the preparation workflow.
The system:
- Organized incoming borrower documents
- Extracted relevant financial information
- Flagged missing or conflicting information
- Linked figures back to source documents
- Retrieved relevant internal policy
- Prepared a structured first draft of the credit memorandum
- Routed exceptions to the appropriate reviewer
Credit policy did not change.
Approval authority did not change.
The institution continued to decide what constituted an acceptable credit risk.
Technology improved the quality and speed of the information reaching those decision makers.
The result
Modeled analyst preparation time declined from approximately 7.2 hours to 4.6 hours per file—a 36% reduction.
Average decision cycle declined by approximately 3.1 days.
Files returned for missing or inconsistent information declined approximately 28%.
At the modeled annual volume, the change would release more than 10,000 analyst hours back into customer-facing and judgment-intensive work.
The value did not come from asking AI to decide who deserved credit.
It came from getting better information to the people accountable for making that decision.
