What to train and what to retrieve
Aranv builds a custom LLM for financial services firms: a large language model (LLM) adapted to your policies, filings, loan files and credit memos to extract fields, classify documents and draft memos in your format, with documentation for model risk review. Your staff keep every lending and compliance decision.
Many finance document jobs can be met by AI document processing with good prompts, and we test that first. Training your own AI model is for what remains: your index, your schema, your memo voice. Few institutions should train from zero, so we normally adapt open-weight base weights, after reading the base model’s license.
Credit policy, rate sheets and regulatory text change, so they are retrieved when a draft is written. Approved-only memos are another trap: train only on files that passed and the model may write every borrower as a strong one. We include declined files where you allow it.
A loan file, from unsorted package to reviewed memo
Where a trained model sits in credit work
- 01 Classify the package Tax returns, appraisals and statements are sorted into your file index.
- 02 Extract with page references Covenants and income figures return with the page they came from.
- 03 Draft in your format The memo follows your headings, and every number comes from extracted data.
- 04 An analyst signs off Credit staff correct the draft, and lending decisions stay with your approvers.
General information, not regulatory advice. Your compliance team decides how any guidance applies to your institution.
What a finance LLM learns from your files
Retrieval can quote a policy. It does little to teach a model your file index, your memo structure or how analysts read a messy statement.
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Loan file sorting
A model trained on your indexed files recognizes your document types, including scanned statements and forms that generic labels confuse.
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Financial field extraction
Covenants, dates, income lines and ratios come out in your schema, with units tested and each value tied to its page.
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Credit memos in format
Drafts use your section order and analyst phrasing, and pull only figures already extracted, so the writer starts from a checkable version.
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Filing change notes
The model compares a borrower’s latest filing with the previous one and writes what changed in house style, for the analyst covering that name.
Evidence your model risk reviewers may request
The April 2026 federal guidance places generative AI outside its scope, yet says a bank’s own risk management should guide controls for tools it does not cover. Your reviewers may still want evidence. We write up intended use, data, tests and known limits for your team to assess.
What we do
- Intended use and excluded uses
- Training data sources and cutoff dates
- Baseline comparison on your test set
- Known limits and failure examples
- Version history and named business owner
Testing for drift after launch
A model that scored well at launch can slip when loan programs, form vendors or markets change. We freeze a test set, rerun it on a schedule you set and after any change, and track how often analysts correct each field. Falling scores go to the model owner.
What we do
- Frozen test set, rerun on schedule
- Correction rates by field and document
- Comparison with the last approved version
- Retraining only after review
How we approach a project
Private hosting is a common choice for loan files, and private deployment covers the servers and access rules. Your files are used for your model. See AI for banks and credit unions for the wider work, and AI governance consulting for the policy around it.
The SR 26-2 letter from the Federal Reserve says its April 2026 guidance supersedes and replaces SR 11-7, so check older references in your own policies. For a 30-minute conversation about your document types, use our contact page.
How we work
- Numbers trace to a source page
- Lending decisions stay with staff
- Documented for your model risk review
How a financial document model is built
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Choose one document family
We start with a bounded set, such as commercial loan packages, with a credit manager as owner.
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Build the exam from booked files
Final figures in your loan system become the answer key, matched back to source pages.
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Beat the baseline or stop
Prompts plus retrieval sit the exam first, then a tuned model after a base-license check. If it does not win, the simple setup stays and we say so.
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Parallel run with analysts
Analysts work as usual while the model runs alongside, and we compare results before workflow changes.
Why Aranv for financial documents
Numbers are checked against sources
A memo figure that does not appear in the extracted data is flagged, because a transposed digit matters more than awkward wording.
Units and periods get tests
We add filings reported in thousands or millions, fiscal years that differ from calendar years and negatives in brackets, which quietly break extraction.
It does not score applicants
The model extracts and drafts. We do not train it to recommend approval, and your compliance staff review which fields it may see.
Review evidence is a deliverable
Intended use, data sources, test results and known limits are written as part of the work, so reviewers need not reconstruct them.
Planning Custom LLM for Financial Services?
Book a 30-minute conversation with the team that would build it.
Custom LLM for Financial Services FAQs
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Can we train AI on financial documents that contain customer data?
Yes, if your information security and compliance teams approve the data path. Loan files hold names, account numbers and income details, so we agree with them what gets masked and where training runs.
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Does a bank LLM fall under federal model risk guidance?
The April 2026 revised guidance says generative AI models are not within its scope, while a bank’s own governance should guide their controls. Your model risk team decides how to treat yours. This is general information, not regulatory advice.
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Can the model approve loans or make compliance decisions?
No. It extracts fields, classifies documents and drafts memos. Credit approvers decide on lending and compliance staff decide on compliance matters, and the review step is part of the design, not an optional extra.
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How do you know the numbers in a draft memo are right?
Each figure must trace to an extracted field and a source page, and anything that cannot is flagged for the analyst. An analyst still checks the final memo before it moves on.
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What causes drift in a financial document model?
Changes in what it reads. New form layouts, new loan programs, revised credit policy language or a shift in borrower mix can all lower results, which is why the frozen test set is rerun on your schedule.
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Who owns a model trained on our loan files?
Your contract fixes ownership before work starts. Our recommendation is that the weights, adapters, training files, test sets and code transfer to your institution once the work is paid for. Limits in the base model’s license carry over.
Let’s talk about your next step
Contact Aranv to discuss your workflow, current tools, and goals. Meet with our team or send an inquiry.
Aranv at a glance
- 9Years in business
- 120+Clients served
- 300+Projects delivered
- 200+Team members
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- We review your goals, current tools and timeline.
- You get a clear next step: a scope, plan or estimate.
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