How do credit analysts review an AI agent’s decisions?

Credit analysts review an agent's decisions in two passes. First the file: read the record, find the deciding bound, reconcile each figure to its source, test the conclusion against policy, and decide with specific reasons. Then the portfolio: sample what the agent approved on its own and watch how those loans perform.

Why do credit decisions need both passes?

A single file can be wrong in visible ways: a misread bank statement, a missed commitment, a weak decline reason. Analysts catch those one at a time.

A portfolio can be wrong in invisible ways. An agent that approves small loans at depth 3 might apply an expense assumption that is slightly generous for one segment, approving loans that each look reasonable and together perform worse than expected. Only a review of outcomes across many decisions shows it. Model risk is supervised in most banking markets, and an agent that makes credit decisions sits squarely in that territory.

What are the steps for a single file?

1. Read the verdict and the deciding bound. A hold under the lender's own limits means the analyst decides. A hold on the source bound means data was missing or conflicting.
2. List the figures. Income, commitments, expenses, existing exposure, security.
3. Trace each figure to its source. Bureau data is signed. Bank data from the account provider is signed. Application form figures are declared. Internal exposure from the core banking ledger is sealed.
4. Recalculate the key ratios the policy relies on.
5. Test against policy and note any exception.
6. Decide within authority.
7. Write specific reasons, especially for declines and conditions.

What does portfolio review look at?

The analyst does not change the agent. They produce the evidence the agent's owner and the credit committee need to change it.

An example: a segment drifting

Take a retail bank whose agent approves small personal loans at depth 3 for existing customers. A credit analyst running the monthly review notices that early arrears on agent-approved loans for customers with variable income are higher than for analyst-approved loans in the same segment. The records show the agent averaged twelve months of income, while analysts in that segment had been using the lowest three months.

Neither approach breaks policy, which is silent on the point. The analyst writes it up with the evidence. The credit committee amends the policy to specify the conservative method, the agent's brief is updated, and the agent is re-certified before resuming depth 3 approvals in that segment. Market detail for this kind of lending is in AI agent training for credit analysts in retail banking in Australia.

What stays with the analyst?

Declines, exceptions, overrides, any decision above the agent's certified depth, and the reasons given to applicants. The agent may gather data, calculate, draft and, where certified, approve inside narrow bounds. Every decision is filed so either kind can be reconstructed later.

Which controls make review count?

• Held files are reviewed one at a time, never in bulk.
• Portfolio review runs on a set cycle and reports to the credit committee.
• Reasons are checked for specificity.
• Analysts' own approvals are sampled by a senior credit officer.

Teams learn this in how to train the credit team to review AI agent decisions; the receivables side is in how to train the credit control team. Neighbouring roles review differently; see how credit controllers review an AI agent's decisions and how fraud analysts do. The role map is in AI agent skills by role.

Where the Academy fits

Pryme Intelligence Academy's Operator track trains analysts to read the decision chain and the provenance of every figure, with module tests set in their own industry and role. Certified Pryme Operator requires a written exam and two weeks of real review decisions signed off by a Pryme assessor. A lender can make approval of held credit files depend on a current certificate, depth by depth.