AI in the Credit Paper and IM Workflow: A Practical, Compliant How-To

Part of the series: How to use AI with client documents in Australian real estate finance

General information, not legal advice. Current as at June 2026.

The regulatory pieces - sovereignty, governance, private-credit recordkeeping, the ADM obligation - all point to the same operating pattern. Here is what that pattern looks like on an actual deal, from documents in to credit paper out.

The five-step pattern

1. Keep the documents onshore

Before anything else: the client documents - IM, financials, lease schedule, valuation, borrower accounts - are processed by a sovereign, onshore system, not a public offshore tool. No cross-border disclosure, no APP 8 exposure, no client data training someone else's model. This is the foundation everything else sits on, and it is non-negotiable for confidential deal material. If you only do one thing from this article, do this.

2. Extract, don't decide

This is the discipline that keeps AI on the right side of the line. Use AI to read and structure, not to make the call:

• Pull the financials into a normalised structure.
• Abstract the leases - terms, expiries, options, rent reviews - into your standardised schedule.
• Compute DSCR and LVR from the extracted figures.
• Surface the data points that feed the credit paper.

The AI assembles the evidence. The credit decision, and the rationale for it, stay with an accountable human. This matters for two reasons: it keeps you inside the "efficiently, honestly and fairly" licensee obligation, and it keeps you out of the "black box" credit-decisioning territory ASIC specifically warned about in REP 798. A reviewer can always see why the deal was graded the way it was, because a person graded it. The AI can still flag gaps, anomalies and recommendations - those just feed the human's judgement, they do not replace it.

3. Keep every number traceable

Each extracted figure should link back to its source document and page. When a valuation-governance question comes - and after REP 820 it will - you want to show the LVR basis and point to the source, not reconstruct it from memory three months later. Traceability turns "trust me" into "here it is," which is the difference between passing and failing the new evidentiary standard.

4. Log what the AI did

Record where automation was used and what it produced. This single log does triple duty:

• It is your operational-risk evidence for CPS 230-style expectations.
• It is the raw material for your 10 December 2026 ADM privacy-policy disclosure.
• It is the audit trail that lets you stand behind an AI-assisted file.

5. Govern it like a risk, not a gadget

AI use sits in your risk framework with a named owner, not per employee at their own discretion. That is the exact gap ASIC and APRA called out, and it is closed by ownership and oversight, not by good intentions.

A worked example

Take a typical CRE facility coming across the desk. The IM lands, along with the valuation, lease schedule, three years of borrower financials, and a DDQ.

The sovereign system reads them. The financials are pulled into a normalised structure; the leases become a schedule with weighted average lease expiry and review dates flagged; DSCR and LVR are computed and shown with their inputs; the DDQ responses are drafted from the documents already on file, each answer pointing back to its source. Within the hour, the analyst is looking at a structured, sourced pack instead of a stack of PDFs.

Then the human does the actual job: reads the structure, tests the assumptions, forms a credit view, and writes the decision. The hours saved are the tedious 80% - the reading, the re-keying, the cross-checking. The 20% that is judgement, and the accountability that goes with it, stay exactly where they belong.

The capability behind it

This is the system we build at Vanillah: a local, agentic AI that lets you chat with your document archive, process the common CRE documents, and automate the creation of new documents from your own templates - credit papers, IM sections, DDQ responses - all onshore, all logged, all traceable. The point is not to replace the underwriter. It is to give the underwriter back their evenings while leaving the judgement, the audit trail and the data firmly in the country and in human hands.

One caveat that underpins all of it

AI is only as good as the documents you feed it. Garbage in, confident garbage out - which is worse than slow manual work because it looks authoritative. Get the data discipline right first; see data quality first.

The bottom line

The compliant workflow and the fast workflow are the same workflow. Keep the documents onshore, use AI to extract rather than decide, make every number traceable, log what happened, and own it as a risk. Do that and AI does the heavy lifting on every credit paper and IM that crosses your desk - without ever putting you in a position you cannot defend.

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Want to see where you stand? Run through the checklist for using AI legally in Australia. Or get in touch about a Local Private AI setup and we'll show you the credit-paper workflow running on one of your own deals.

This article is general information and is not legal or compliance advice. Confirm your obligations with your own advisers.