Most AI ideas in lending fail for a simple reason: the team starts with the technology before it defines the workflow. This one-page template helps lending teams pressure-test an AI use case before a pilot starts, separating strong use cases from expensive distractions by forcing clarity on ambiguity, consequence, explainability and controls.
The questions are practical, not theoretical. They are meant to surface whether the workflow is ambiguous or structured, how consequential the output is, what controls would be required, and whether rules, code, or analytics might solve the problem more cleanly.
The goal is to make sure the right workflows move forward for the right reasons.
What workflow are we trying to improve?
What specific problem exists today?
What is happening now that is too slow, too manual, too inconsistent, or too blunt?
What business outcome would improve if this worked?
What kind of task is this?
What best describes the input data?
Where does ambiguity exist in the workflow today?
If this output is wrong, what happens?
Is this workflow fault tolerant or fault intolerant?
Why?
Will this output need to be explained to any of the following?
Does the workflow require reason codes, rationale, or a clearly reviewable logic path?
What governance artifacts would need to exist before production?
Could this be solved with rules or standard code instead?
Could this be solved with a governed analytic output instead?
Why is AI being considered?
What does AI do here that simpler tools cannot do as well?
If AI is used, what controls would sit around it?
What must remain fully deterministic and policy-bound?
Who signs off on the control design?
Will any customer data move to a third party?
Are there privacy, data-flow, or security concerns to resolve first?
What monitoring would be required in production?
What happens if the model output is missing, wrong, or unclear?
What value would this create if it worked?
What costs would it introduce?
What is the simplest effective option?
Recommended tool approach
Why is this the right approach?
What is the next step?
If this template shows that your workflow needs more than rules, but still demands strong governance and explainability, that is typically where Carrington Labs can help. We work with lenders to add a cash flow underwriting and credit risk analytics layer alongside existing systems, so teams can use richer signals for approvals, exposure, and monitoring without giving up policy control.
If this review points to a need for stronger risk signals without losing policy control, contact us to see how Carrington Labs could fit into your lending stack.
Leave your details below and our team will schedule a call to discuss your business and how Carrington Labs can help.
