Sell bank and lender data to AI companies

Why AI companies want banking and lending data right now

AI is moving quickly into financial services. Agents are being built to prepare credit memos, check loan files for missing conditions, answer servicing questions and draft the follow-up a loan officer would otherwise write by hand. To do that well, they need to see how a lending team actually works – the back-and-forth between a loan officer and an underwriter, how an exception gets approved, how a late payment turns into a workout plan.

Almost none of that is public. Regulatory filings and call reports show the results of lending, not the work behind it. The reasoning lives in email threads, credit memos, underwriting notes and the internal chat where a processor asks why a file is stuck. That is why lending ranks among the industries in highest demand today, alongside law firms and construction.

Selling bank data to AI companies in practice means licensing it. The buyer gets a limited right to train on an agreed, cleaned selection, and you keep the records.

What carries the value

The value comes from a lending team's working record across the same set of connected systems, kept over years. The five core systems matter most, with the banking-specific tools adding depth:

Value goes up with three things. First, years: three years of history is the usual floor, and five or more is better. Second, completeness: when email, files, chat, CRM and accounting are all included for the same team, the record is far more useful than any one system alone. Third, the mix of work – consumer, mortgage, commercial and agricultural lending each add something different.

Compliance and operations communication is part of that value too. How a team handles an exam finding, documents a fair-lending review or resolves a BSA alert in its workflow says a lot about how a regulated business runs. The personal and account details in those records are removed; the process stays.

Who fits and how to count your staff

• Community banks with commercial, agricultural or residential lending.
• Credit unions with consumer and mortgage lending and member services.
• Specialty lenders – equipment finance, SBA lending, auto, mortgage brokers and servicers, commercial real estate lenders.
• Banks merging or closing a line of business, where records may otherwise just be archived – see shutting down a company.

Lending deals typically start from about 15 office staff. Count the people whose work lives in the systems above: loan officers, processors, underwriters, closers, servicing, compliance, operations and finance. Branch tellers count when they work in the same email and chat, but the core of the value is usually in the back office. Bigger teams get bigger offers, and teams of 100 or more office staff get the largest.

The institution needs to own its records. When a vendor, a franchise structure or a parent company controls the systems, that is checked early. Internal communication should mainly be in English.

What is never part of a deal

Sensitive content is filtered inside each system, not by leaving whole systems out:

• Account and card numbers are never part of a deal, in any system.
• Customer identity – names, addresses, Social Security numbers, dates of birth and contact details – is removed or replaced before delivery.
• Credit reports and bureau data that belong to a third party stay out.
• HR and personnel records are filtered out.
• Material under a confidentiality agreement with a partner, investor or regulator is assessed and usually excluded.

How this applies to your institution depends on your privacy notice, your agreements and your records, so it is assessed case by case. This describes how it generally works today, not legal advice. There is more in data privacy and AI training.

Leaving out a whole system – for example all internal chat – lowers the offer, because the buyer loses the connection between conversations and outcomes. Filtering inside the system keeps that connection.

What lenders typically receive right now

Smaller companies in high-demand industries are quoted individually. These are indicative ranges from what we see in the market right now, not offers – the real number comes in a written offer after a review, and depends on your industry, years of history, which systems are included and demand at the time.

Because lending is in the highest demand right now, banks and lenders with complete systems and long history tend to land toward the top of their band. Our data value estimator gives a first estimate in about two minutes.

How a deal works

1. You describe your institution – staff, systems and years of history. It takes about two minutes and nothing is uploaded.
2. We review fit, ownership and demand, and come back within 48 hours on business days.
3. You approve a written offer – or say no. Nothing is shared before you approve.
4. Clean-up and delivery. Account numbers, customer identity and other excluded content are filtered inside each system, then the agreed records are delivered and payment is made.

A decision maker who can sign within about two weeks of the first call helps a deal move faster. Having it assessed costs nothing – we only get paid when a deal goes through. Read more about how it works. For the full picture across industries, see how to sell data to AI companies.