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Loan review AI governance & implementation playbook
The practical guide to reducing repetitive loan review work without sacrificing consistency, accountability, or professional judgment.
Download the free playbook
Loan Review AI Governance and Implementation Playbook
31%
reported average efficiency improvement in new loan reviews at Old National Bank

51%

are exploring or planning to use AI in the near future

57%

expect AI investment to increase over the next 12–24 months

82%

see value in documentation review and summarization

71%

see value in narrative generation

47%

cite data quality and lack of internal expertise as barriers

Why this guide
AI adoption is moving from curiosity to capability.
The question isn't whether AI can help loan review. It's how to introduce it in a way that creates efficiency while keeping the reviewer accountable for the work that matters most.
01
Start with the right work
Prioritize repetitive, easy-to-validate tasks such as document review, summarization, narrative generation, quality-control support, and scoping support.
02
Build governance around each use case
Define what AI is assisting with, what information it can access, what output it provides, who validates it, and what evidence must be retained.
03
Prove value before you scale
Use a controlled 90-day rollout to test output quality, usability, reviewer trust, and efficiency before deciding whether to expand, refine, or pause.
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Core principle
AI should assist with evidence gathering, organization, and first-draft work. Reviewers remain responsible for validating outputs, applying context and mitigants, and owning final conclusions and documentation.
Inside the playbook
A practical path from AI interest to controlled implementation.
Get a framework your team can use to select, govern, test, measure, and refine an AI-assisted loan review use case.
1
Define a narrow use case
Focus on a practical workflow with meaningful reviewer pain points.
2
Set source and governance controls
Define approved source material, validation responsibilities, and evidence requirements.
3
Test with a controlled reviewer group
Compare AI-assisted work with the current process and capture reviewer feedback.
4
Measure quality alongside efficiency
Track accuracy, completeness, reviewer confidence, and capacity impact before scaling.
90-day implementation roadmap
Start small. Learn fast. Expand based on evidence.
Move from one practical use case to a governed pilot, then make an evidence-based decision about what comes next.
1
Select
Weeks 1–3  •  Choose one practical use case
2
Prepare
Weeks 4–6  •  Set up documents, workflow, and controls
3
Test
Weeks 7–10  •  Test with a controlled reviewer group
4
Refine
Weeks 11–12  •  Improve the process before scale
5
Expand
After testing  •  Scale selectively based on evidence
Key takeaway
“The most successful AI-assisted loan review programs make reviewer expertise more scalable.”
The reviewer remains vital to the process.
Download the free playbook
Turn AI interest into a practical next step.
Get the Loan Review AI Governance & Implementation Playbook for practical guidance on selecting use cases, establishing controls, validating AI-assisted work, and building a phased 90-day implementation plan.
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Governance questions every AI-assisted workflow should answer
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Human-in-the-loop validation and quality-control guidance
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A phased 90-day roadmap for controlled implementation
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