Short answer: A loan officer AI assistant should be evaluated as a human-reviewed workflow: identify the record context it may use, ask it to show its output with non-sensitive examples, name the person who reviews any borrower-facing action, and document what happens when the output is incomplete or wrong.
Scope note: This is a software-evaluation checklist, not product specifications, legal, licensing, privacy, security, advertising, underwriting, credit, or compliance advice. An organization should define its own approval process and confirm its current configuration and requirements before relying on an AI-assisted workflow.
The five review questions
| Review question | What to ask the provider to demonstrate | Human review point |
|---|---|---|
| What context is in scope? | A non-sensitive sample record and a clear explanation of which fields, activity, and roles the workflow is allowed to use. | Confirm that the context shown is appropriate for the task before acting on an output. |
| What can the assistant prepare? | A factual summary, draft, reminder, task suggestion, or retrieval example using a representative workflow. | Check the underlying record and revise the output before treating it as a business communication or decision input. |
| Who owns the next action? | How a suggested next step becomes an assigned task or a reviewed draft, including a visible owner and timestamp. | A named person accepts, changes, or declines the suggestion. |
| What happens when information is incomplete? | A duplicate, missing, conflicting, or stale record and the correction path. | Pause the planned action, correct the record, and preserve a clear explanation of the resolution. |
| Where are limits documented? | Current product documentation, account roles, data-use questions, and the route for support or policy review. | Keep the implementation record aligned with the team's own approved process. |
Use representative cases, not a generic prompt
Bring the same cases to each software discussion: a new inquiry, an active relationship record, a past-client or partner record, a duplicate or incomplete record, and a record where the planned communication should stop. Use non-sensitive examples for an initial evaluation. The goal is to see context, output, assigned follow-up, review, and correction together rather than treating a fluent answer as proof of a workflow.
The AI mortgage CRM capability index separates capability questions from evidence and limits. The MAIA human-review guide gives a BNTouch-specific discussion of accountability boundaries.
Recorded BNTouch setup reference
This public 2024 walkthrough shows the MAIA enablement path and example company context preparation. Use it to ask better questions in a current product review, rather than as proof of present configuration or behavior for every account.
Source: BNTouch: Enable MAIA in BNTouch.
Scope of this recorded source: It is a dated setup example. It does not establish current feature availability, permissions, account configuration, data handling, commercial terms, or an approved process for another organization. Confirm those details in a live review.
Keep the review framework practical
The NIST AI Risk Management Framework is a useful public reference for organizing questions about governance, context, measurement, and management. It does not prescribe a mortgage CRM configuration or replace an organization's own approval process.
Bring five non-sensitive workflow examples to a BNTouch review and ask to see the exact record context, suggestion, owner, approval point, and correction path your team needs.
Further reading: AI mortgage CRM capability index; MAIA human-review guide; NIST AI Risk Management Framework.
