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Use cases/Financial services/Credit file assembly

AI for credit file assembly in Australian lending

Credit file assembly means the documents in a loan application are read, checked against what the applicant declared, and set out as draft credit paper in the lender's own template. Every figure shows the document and page it came from. The credit assessor reviews the file and makes every decision.

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Supports
Credit assessor
Reads
Payslips, Bank statements, Tax returns, Valuations, Borrower financials
Runs on
Your servers, on premises
Connects to
nCino, Temenos, Infosys Finacle, Ultradata
Decides
A person, every time

Architecture

Everything runs on your servers

We build it on an open-weight model such as Llama or Mistral, served with vLLM on NVIDIA GPUs in your server room, data centre or colocation. Retrieval runs on PostgreSQL with pgvector or Qdrant, orchestration runs on LangGraph, and the system reads from and writes back to the origination platform you already run.

  1. SourcesPayslips / Bank statements / Tax returns / Valuations / Borrower financials
  2. Your serversRead each document → Check against application → Draft credit paper
  3. ReviewCredit assessor
  4. OutputDocuments read and checked / Differences listed first / Credit paper in your template / Covenant monitoring

It takes four jobs off the credit assessor

  1. 01

    Documents read and checked

    Payslips, bank statements, tax returns and valuations are read on arrival. Each one is compared with the income, expenses and security the applicant declared.

  2. 02

    Differences listed first

    Where a payslip, a statement or a valuation disagrees with the application, the difference is listed with both sources. The assessor starts with the lines that need judgement.

  3. 03

    Credit paper in your template

    The credit memo is drafted in the lender's own format. The source document and page sit beside every figure, so the assessor can open the file and check it.

  4. 04

    Covenant monitoring

    Borrower financials are read when they arrive and compared with the covenants in the facility agreement. Each breach or near breach is listed with the clause behind it.

It goes live in five steps

  1. Step 01

    Collect past files and credit paper

    Closed applications with their documents, the credit memos written for them and the decision made. This is the test set and it decides the design.

  2. Step 02

    Write down the checks

    We sit with the assessors and list the checks they run by hand. Each check becomes a rule the system applies and shows.

  3. Step 03

    Test against decided files

    The system reads files that already have a decision and the output is compared with the credit paper the assessor wrote. The results set which document types go first.

  4. Step 04

    Run in shadow

    Assessors work as usual while the system drafts beside them. They mark where the draft was wrong, and those marks feed the evaluation set.

  5. Step 05

    Assessor reviews and decides

    The draft reaches the assessor with differences listed first. Every credit decision, approval and exception stays with the person, and the log records what the system read and drafted.

Software or a custom build

If your volume is low and your applications are standard, use the workflow in your origination platform first. nCino, Temenos, Infosys Finacle, Ultradata, Simpology and ApplyOnline all carry credit workflow, and Data Action carries loan application workflow. Equifax and Experian (formerly illion) supply the bureau data. A custom build earns its cost when income documents arrive in many formats, when credit paper follows a house template, when covenants sit in agreements nobody has indexed, or when borrower documents cannot leave your environment.

nCino / Temenos / Infosys Finacle / Ultradata / Data Action / Simpology / ApplyOnline / Equifax

In practice

We build this on servers the lender owns. The model, the document store and the logs sit inside your network, and application documents are not sent to a public AI service. The pattern is set out in RAG on private infrastructure and private LLM deployment architecture. The work itself falls under LLM integration and RAG.

An example engagement shows how we put an open-weight model inside a bank network so credit analysts can search covenant language, financial statements and credit memos, with the source page cited on every answer. APRA CPS 234 also applies where a third party manages information assets, so the build is documented for your risk team to review before it goes live.

Where the human sits in the file is a design decision, and where to put the human covers it. The wider picture for banking and real estate lenders has its own page.

Example engagements

All case studies →
01A private LLM inside a bank's network for its credit analystsBanking / Example

Questions

What is AI credit file assembly?

It is the reading, checking and drafting that comes before a credit decision. The system reads payslips, statements, tax returns and valuations, checks them against the application and drafts the credit paper with a source beside every figure. The assessor makes the decision.

Does the AI make the credit decision?

No. It reads the documents and drafts the paper. The credit assessor reviews the file, makes every approval, decline and exception, and signs it off as they do today. Anything the system is unsure of is listed with the reason.

Can it work with nCino or our loan origination system?

Yes. It reads from and writes back to the origination platform you already run, whether that is nCino, Temenos, Ultradata or another. That platform stays the system of record.

Where do the borrower documents go?

Nowhere outside your environment. The model runs on your own servers and the documents are read there. Staff see through the system only what they can already open in the source system. We document what it reads and produces for your risk team.

Industries where this comes up

Banking / Real estate

More financial services use cases

Document search / AML alert triage / Claims triage / Month-end close / Audit evidence matching / Advice file drafting / Lease abstraction / KYC and onboarding / Complaints handling / Regulatory reporting / Underwriting submissions / Board and management reporting

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