LoanCraft

Does LLM AI Have Enough "Algorithmic Consistency" to Transform Lending? Part Three

By LoanCraft Team May 05, 2026

Testing Methods and Principles for AI in Lending

In part one, we discussed how AI LLM models do not yield "algorithmic consistency." Traditional computer systems are deterministic; for the same inputs, one can expect the same outputs 100% of the time. But AI is not deterministic, and the answers might vary.

In part two, we discussed ways that lenders can manage this difference. The key is to find ways to measure the consistency of the AI models and to set out requirements for consistency that make an AI model a viable substitute. In this third part, we discuss some testing methods and principles that can be applied.

A Toy Test

A little toy test can demonstrate the challenge of testing AI models for lenders. We put together eight standard underwriting parameters for a mortgage (FICO, LTV, DTI, Rate, and some others). Three times, we asked the AI models with identical prompts, in the space of a minute or so, to rate the credit on a scale of 1 to 100:

  • ChatGPT: 58, 64, and 72. The response varied by 24%.
  • Gemini: 62, 64, 62. (A little more consistent, but still varied).

The Challenge of Testing a Changing "Black Box"

"Black box" is a common term we use to refer to a system for which we know nothing about what goes on inside. In many ways, LLM AI models are black boxes, even to their developers. Contrast this with testing a "clear box" When you know what is changing, you can narrow your testing to the components that are changed. But when a black box changes, you essentially must repeat your wholesale testing of all the input-to-output mapping.

Test Failure is More Challenging

In traditional software, a test failure is usually binary: a line of code is broken or a logic gate is flipped. With LLMs, 'failure' is often a statistical drift rather than a hard crash. When a model that previously scored a credit profile at a 64 suddenly begins scoring identical profiles at a 42, there is no specific 'line of code' to repair. This creates a unique burden for lenders: you cannot simply patch a black box. Instead, you must diagnose whether the failure is a result of prompt sensitivity, data drift, or stochastic temperature spikes.

Testing Principles

  • Adhere to your expectations: Define 'Golden Sets'�a collection of inputs where the output is non-negotiable. If your credit policy dictates that a specific DTI and FICO combination is a 'Deny,' the AI must consistently reflect that.
  • Avoid ad hoc "learning": Resist the urge to fix individual errors with 'band-aid' prompts. This leads to 'prompt brittleness.' Instead, address consistency through systematic architectural controls.
  • Large tests must be run frequently: Because AI providers push updates (often unannounced), a model that worked on Monday might behave differently on Tuesday. Obtain measurements or indicators of model change, such as monitoring Kullback-Leibler (KL) divergence or cosine similarity, to trigger new large-scale regression tests.

Testing Methods and Tools

  • Generate a large set of test items: Create thousands of synthetic 'borrower personas' spanning the entire spectrum of your lending criteria. Use AI to expand and refine the set by asking it to generate 'edge cases.'
  • Allocate necessary resources: Testing is no longer a 'final check' before launch; it is a continuous operational expense. Shift some of the 'creation resources' to become testing resources.
  • Create highly powerful, high-volume testing tools: To manage the non-deterministic nature of AI, you must run 'Monte Carlo' style evaluations. Don't test an input once; test it 50 times and measure the standard deviation of the scores.
  • Use AI to test AI: Leverage a 'Challenger' model to audit the 'Champion' model. A separate LLM can be programmed with your specific lending handbook to grade the primary model's responses for policy compliance.

Conclusion

To conclude, lenders can gain enormous efficiency from AI models but must reckon with a completely different paradigm for consistency and testing. The prudent lender will develop strict acceptance standards for AI models and will constantly test to ensure they remain consistent with those standards.

LoanCraft has been applying cutting edge technology to lending processes for over twenty years. Learn more at loancraft.net.