01GLBA
Loan file extraction
Income, assets and conditions pulled from applications and statements into your LOS.
LLM.co · Private AI & Custom AI DevelopmentPrivate AI for financial services
Loan file extraction, KYC and AML review, and research on internal data, deployed where nonpublic personal information is allowed to live.
FIG. — IBM 704, NASA Ames, 1958LLM.co
Financial institutions run on documents: applications, statements, tax returns, agreements and filings. Reading them is slow, and sending them to a public AI service raises questions your examiners will ask.
We build extraction, review and research tools that run in your environment, with model risk documentation your second line can work with.
| Systems | Loan origination systems, core banking exports, data warehouses |
|---|---|
| Controls | GLBA safeguards, SOX change control, audit trail |
| Model risk | Documentation aligned to SR 11-7 style reviews |
| Deployment | On-premises or private cloud |
What we build
Common financial services builds.
02GLBA
KYC & AML review
Entity documents summarized and checked, with exceptions routed to analysts.
03GLBA
Internal research
Questions answered across memos, filings and models with citations.
04GLBA
Reporting drafts
Commentary and variance notes drafted from your own data.
Why institutions choose private AI for banks and lenders
Banks, credit unions, lenders and asset managers hold nonpublic personal information that GLBA requires them to safeguard. Sending loan files, statements or account data to a public AI service adds a new third party, a new data flow and a new set of questions from examiners and the board. Third-party risk management reviews for a model vendor can take longer than the project itself.
Private AI for banks runs open-weight models inside your own data center or cloud account. Customer data stays where your information security program already applies, behind the same access controls, encryption, logging and change management. The AI system becomes one more internal application your teams know how to govern.
AI for financial services workflows that fit today
The best early uses involve reading documents at volume and producing structured output that an analyst checks.
- Loan file extraction. Income, assets, liabilities and conditions pulled from applications, tax returns and statements into your loan origination system, with field-level confidence scores.
- KYC automation. Formation documents, ownership charts and IDs summarized and checked against your onboarding checklist, with exceptions routed to an analyst.
- AML alert review. Transaction context and prior case notes gathered into a draft narrative for the investigator to complete.
- Internal research. Questions answered across credit memos, policies, filings and research notes, with citations.
- Reporting drafts. Variance commentary and management notes drafted from your own data.
Model risk management and examiner expectations
Supervisory guidance on model risk management, such as SR 11-7, expects institutions to document a model's purpose, data, limitations, validation and ongoing monitoring. Generative AI systems fit that framework when they are built for it. We deliver a model document with each system that covers intended use, data sources, evaluation results on your own files, known failure modes and the monitoring plan.
Every request is logged with the user, the input, the documents retrieved, the model version and the output. SOX change control applies to prompts, models and code the same way it applies to other production systems. Your second line and internal audit can review the system with the evidence they already ask for.
What to pilot first, and what to avoid
Start where volume is high and fields are well defined, such as income and asset extraction on one loan product or document checks in commercial onboarding. Score the system against completed files before it touches live work.
Avoid first projects that make or recommend credit decisions, set pricing or decide whether to file a suspicious activity report. Those uses raise fair lending, adverse action and BSA questions that need far more evidence and review. Keep AI on extraction, summarization and drafting, with a person making the decision.
How LLM.co delivers it
Our custom AI development for financial institutions deploys on-premises or in your cloud tenancy, with logs sent to your SIEM. You own the code, prompts, evaluation sets and any fine-tuned weights.
How it works
Four steps, each one reviewed.
01
Pick the file
Start where volume is high and fields are well defined.
02
Extract & verify
Field-level confidence scores and human review for low-confidence values.
03
Document the model
Purpose, data, limits and monitoring written up for model risk management.
04
Deploy privately
On-premises or in your cloud tenancy, with logs to your SIEM.
Questions
Common questions.
Can you support model risk management review?
Yes. Each system ships with documentation of purpose, data sources, evaluation results on your own files, limitations and the monitoring plan. Your model risk team can validate it like any other model under SR 11-7 style review. Validation itself remains with your second line or an independent validator.
Where does customer data go?
It stays in your environment. Models run on your infrastructure or in a cloud account you control, and nothing is sent to an outside AI provider unless you approve a specific, non-sensitive use. Your existing GLBA safeguards apply to the system.
Does private AI for banks satisfy GLBA?
No tool satisfies GLBA on its own. Private deployment keeps nonpublic personal information inside the boundary your information security program already protects, with access controls, encryption and audit logs. Your compliance team decides how the system fits your safeguards program and risk assessment.
Can AI automate KYC and onboarding review?
It can do much of the reading. KYC automation extracts entity details, beneficial owners and document dates, checks them against your onboarding checklist and flags gaps. An analyst reviews exceptions and makes the decision. Screening against sanctions lists stays with your existing screening tools.
Should AI make credit decisions?
We advise against starting there. Credit decisions carry fair lending and adverse action obligations that require clear, explainable reasons. A safer first step uses AI to extract and organize the file so underwriters decide faster, with the decision and its reasons recorded by a person.
How does this simplify third-party risk management?
Because models run in your environment, there is no external model provider receiving customer data. Your vendor review covers LLM.co as a development partner rather than an ongoing data processor, and you own the code and weights after delivery.
Which systems do you integrate with?
Loan origination systems, core banking exports, document management systems and data warehouses, through their supported APIs or file exports. Extracted fields carry confidence scores, and low-confidence values go to a review queue before they reach the system of record.
Related
More industries.
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