Adopting AI underwriting does not need to begin with a full transformation of the lending operation.
A focused pilot allows a bank or lender to test the technology using a defined product, customer segment, or application channel.
The institution can compare AI-supported recommendations with the current process, measure operational results, assess decision quality, and identify implementation requirements before expanding.
Step 1: Define the business problem
An AI pilot should begin with a clear operational or credit challenge.
Examples include:
- Loan decisions take too long
- Credit analysts spend too much time reading documents
- Application volume is growing faster than the team
- Decision consistency varies between reviewers
- Missing documents create repeated delays
- Fraud checks are too manual
- Partner applications need faster responses
- Management has limited visibility into decision reasons
The pilot should focus on one or two measurable problems.
"Testing AI" is not a sufficient objective.
"Reducing average manual review time for personal loan applications" is much clearer.
Step 2: Select the right lending product
The first product should be meaningful enough to demonstrate value but controlled enough to manage risk.
Good pilot candidates often have:
- A defined application process
- Repeated document types
- Clear eligibility criteria
- Sufficient historical applications
- Measurable lending outcomes
- A manageable number of policy exceptions
- Support from the product and credit teams
Possible pilot products include:
- Personal loans
- Payroll-linked loans
- Small business working-capital loans
- Equipment finance
- Merchant finance
- Leasing
- Hire purchase
- Microfinance products
Starting with the institution's most complex lending product may make the pilot unnecessarily difficult.
Step 3: Establish the baseline
Before testing the new process, the lender needs to understand current performance.
The baseline may include:
- Average time from application to decision
- Manual review time per application
- Applications processed per analyst
- Percentage of incomplete applications
- Number of follow-up requests
- Approval rate
- Decline rate
- Policy exception rate
- Fraud or inconsistency rate
- Cost per application
- Early repayment performance
Without a baseline, the institution cannot determine whether the pilot improved the process.
Step 4: Define the AI workflow
The lender should decide which stages the platform will support.
A complete workflow may include:
- Application and document intake
- Data extraction and structuring
- Identity and income verification
- Bank statement analysis
- Credit history review
- Fraud and inconsistency checks
- Risk scoring
- Explainable recommendation
- Human review
- Final decision and audit record
Lendavium is designed to cover the process from application intake and verification through risk recommendation and final institutional review.
The pilot does not need to automate every stage immediately. It should test the stages most closely connected to the original business problem.
Step 5: Decide how humans will participate
The institution should define the human review model before the pilot begins.
Options include:
Shadow mode
The AI generates a recommendation, but the existing underwriting process remains responsible for the final decision.
The recommendations are compared afterward.
Decision-support mode
Analysts receive the AI recommendation and supporting signals before making the final decision.
Limited automation mode
Selected low-risk applications may move automatically, while other applications require human approval.
Shadow mode is often a useful starting point because it allows the institution to evaluate recommendations without immediately changing decision authority.
Step 6: Select the pilot data
The pilot may use:
- Historical applications
- Current live applications
- A combination of historical and live cases
- Approved and declined applications
- Performing and non-performing loans
- Human-overridden applications
Historical testing can show how the system would have evaluated previous cases.
Live testing shows how the platform operates within the real application workflow.
The dataset should represent the actual customers and risk profiles the institution expects the system to assess.
Step 7: Define success metrics
Pilot metrics should cover three categories.
Operational performance
- Decision time
- Manual review time
- Applications processed
- Document extraction accuracy
- Number of follow-up requests
- Analyst productivity
Decision performance
- Agreement with existing decisions
- Approval and decline distribution
- Number of escalated cases
- Reason for recommendation differences
- Performance by customer segment
Portfolio and risk performance
- First-payment performance
- Delinquency
- Default rate
- Early repayment
- Performance of AI recommendations
- Performance of human overrides
Lendavium publishes results from its use on an operational loan portfolio, including repayment and default comparisons between AI-approved loans and cases where people overrode the recommendation. Those results demonstrate the importance of connecting underwriting recommendations with actual repayment outcomes.
Any institution conducting a pilot should evaluate results using its own portfolio, policies, and risk definitions.
Step 8: Review disagreements carefully
The most valuable pilot cases are often those where the AI and human analyst disagree.
Each disagreement should be reviewed to understand:
- Did the AI identify information the analyst missed?
- Did the analyst have relevant context unavailable to the model?
- Was the institution's credit policy correctly configured?
- Was the input data complete?
- Was the human decision based on a documented exception?
- How did the loan perform afterward?
The objective is not to prove that either the AI or the analyst is always correct.
The objective is to understand how the combined process can improve.
Step 9: Gather feedback from users
Credit analysts and operations employees should be involved throughout the pilot.
Their feedback can reveal whether:
- The recommendation is easy to understand
- Important information is clearly highlighted
- The system creates additional steps
- Alerts are useful or excessive
- Analysts trust the data presented
- The workflow matches existing responsibilities
- Further training is required
A technically accurate platform can still fail if it does not fit the daily work of the credit team.
Step 10: Decide how to expand
After the pilot, the institution can choose to:
- Expand to more applications within the same product
- Add additional lending products
- Connect more data sources
- Introduce automated applicant follow-up
- Automate selected low-risk approvals
- Add further specialist analysis
- Integrate the platform with existing systems
- Continue testing before deployment
Expansion should be based on measured results rather than the initial enthusiasm surrounding the technology.
A pilot creates evidence, not assumptions
AI underwriting should not be adopted based only on general claims about speed or efficiency.
A structured pilot gives the institution its own evidence.
It shows how the technology works with the lender's customers, documents, policies, analysts, and risk appetite.
That makes the pilot one of the most important steps in responsible AI adoption.
Explore related Lendavium capabilities
See how this fits the broader product: How It Works, Proven in Production, For Banks and Credit Unions, and Request a Demo.