PROPOSED AI APPLICATION

Lender product evidence assistant

Advisers struggle to compare approved product terms

Loan sourcing and DSA agenciesSpecialist evaluationProposed solution scope

The problem and the proposed outcome

Application: Loan sourcing and DSA agencies — Lender product evidence assistant
Problem to solve: Advisers struggle to compare approved product terms
Target buyer: Loan sourcing agencies, case coordinators
Intended use: Employee / expert own workflow; internal business solution
Benefit to test: Test whether evidence lookup and response preparation become faster while correctness remains acceptable.
Potential delivery: Internal application, reviewer dashboard, private assistant or authorized user portal; choose one surface after validating the workflow.

AI method: Evidence-grounded knowledge assistant / RAG
Method code: R
AI contribution: Use semantic retrieval and a language model to answer from approved sources, attach source references, and abstain when evidence is missing.
Output artifact: Answer draft with supporting source passages and an unresolved-question flag.
Core modules: Approved source intake → semantic retrieval → evidence-linked answer → reviewer feedback
Simple baseline: Manual document search, curated FAQ and keyword search.
Why test AI: Could help with semantically varied questions over many documents; retain the baseline if AI does not improve usefulness or handling time.

Pilot track: Specialist evaluation
Required data: Current approved lender documents; applicant-stated needs
Data readiness: A current approved corpus, access rules and representative questions with reviewer-approved answers are needed.
First development step: Select an approved corpus and create a small reviewer-labelled question set; measure keyword/FAQ performance first.
Initial MVP scope: One defined task, one permitted dataset and one reviewer workflow. Manual upload or authorized export first; no unattended external action. Validate the model before live integration.
Demo data: Synthetic or licensed public samples may illustrate the workflow. Real benefit and model performance need representative authorized held-out data.
Buyer validation question: Ask Loan sourcing agencies, case coordinators: show a recent case where advisers struggle to compare approved product terms, record the present effort or error, and confirm whether the required data can lawfully be used.

Primary success metric: Qualified-reviewed source relevance
Model evaluation: Measure answer correctness, grounding, source relevance, abstention and unauthorized-source exclusion on held-out questions.
Metric target: Set after baseline measurement; no improvement or accuracy result asserted.

Specialist review: Yes
Human review boundary: Qualified domain specialist reviews evidence, interpretation and any consequential action. Start with controlled offline or shadow evaluation; no autonomous clinical, financial, employment, industrial or safety decision.
Domain review: Use lawful authorized evidence and permitted features; qualified staff review substantive decisions, explainability, fairness and applicable rules.
Related micro-ERP catalogue IDs: IWA-B-228F791B6EA4; IWA-B-D91CA1FE6706; IWA-B-51F0FB197C11
Integration status: Potential segment link only; no product integration implemented.