Fintech and Health Tech Company Data: What AI Labs Can License
AITrainingDataBroker.com
Fintech and
Health Tech
Data for AI Labs
Data for AI Labs
What can be licensed, what stays locked away, and the rules to check first
Jimmy Lin
Fintech and Health Tech Data for AI Labs
Fintech and health tech companies hold some of the most private data there is, so you might think an AI lab can not touch any of it. The customer side is exactly right, and that part is never shared. But the way the company itself runs is a different story, and that is what an AI lab may want to read. In this video I will show you which data is locked away, which data can be listed, and the rules to check first. We begin with a vault.
The video
Full script
Fintech and health tech companies hold some of the most private data there is, so you might think an AI lab can not touch any of it. The customer side is exactly right, and that part is never shared. But the way the company itself runs is a different story, and that is what an AI lab may want to read. In this video I will show you which data is locked away, which data can be listed, and the rules to check first. We begin with a vault.
Most people think that private data means an AI lab can see nothing, and that is not quite true. The customer side is locked away and is never part of a license. The company's own work is a different matter, from its support tickets to its incident chat, and that is where an AI lab looks. This is general information and not legal advice. First, let me tell you who I am.
I'm Jimmy. I have worked in data at Fortune 500 companies, as a data analyst, a data engineer and a software engineer, and I am trained in data science. I am also a Canadian military veteran, where I worked on data analysis and reporting.
If you want to sell your data, I am your broker and technical advisor. I bring your data to several AI companies, and I guide your team through the technical side, including compliance. You do not pay anything: AI companies pay my fee.
Already shopping your data around? Contact us anyway. We can still work through the terms with you and help you negotiate the best price.
If you are buying data for an AI lab, I find the right data and see it through to delivery, so you can focus on your research.
So what does a fintech or health tech company actually hold? Fintech means companies in payments, lending and money advice, and health tech means apps, telehealth and software for patient files. Both sit on 2 piles of data. One pile is the customers' data, such as accounts, transactions, diagnoses and messages. The other pile is the company's own work, such as product plans, support tickets and incident chat, and only the second pile is ever listed.
Here is how the 2 piles look side by side. On the shelf sits the company's own work: product plans, support tickets, incident chat and test write-ups. In the vault sit the accounts, the transactions, the diagnoses and the messages between patients and care teams, and the vault is never opened for a license. For the shelf work, we de-identify your data, which means we remove the customer names and details first. But there is a catch: shelf work still needs cleaning before anyone sees it.
For financial companies, the starting point is a federal rule from the US Federal Trade Commission, called the Safeguards Rule. It says that covered financial companies must protect customer information, which means private details about their customers, and it makes them hold their service providers to the same standard by contract. The rule reaches many companies that are not banks, so for a fintech the customer information stays locked away from the start.
Health tech has 2 sets of rules, and which one applies comes down to one question: is your product covered by HIPAA, the US health privacy law? If it is, HIPAA governs the patient data. If it is not, as with many health apps, the US Federal Trade Commission has its own Health Breach Notification Rule, which was updated in 2024 to cover health apps. Whichever applies, the patient data stays in the vault.
Now, what can an AI lab learn from the shelf? Take a payment dispute. A customer reports a charge, support checks the case and passes it on, engineering finds the cause and ships a fix, and support replies. A thread that follows all of those steps, with the customer's details removed, shows how a team solves a problem across 3 groups. That is the kind of work that can be listed, once the data is de-identified.
Put it together and you get 6 checks, and each one has an owner. Legal confirms that the customer data is left out and reads the partner and bank terms. Security confirms that the data is de-identified and that access is limited. The product team confirms that only the company's own work is listed and that pricing talk is set aside. But the list will not hold up unless every check has a named owner, so write the names down. Then your team signs off, and your counsel decides what applies to you.
Take an invented example, a health app with 45 employees and 6 years of history. It sorts its data into 12 kinds. 7 are patient data and messages, and those stay locked. The other 5, the product plans, the support tickets, the incident chat, the test write-ups and the onboarding notes, are listed. So 5 of the 12 can go in front of an AI lab, and each of the 5 is de-identified first.
Now do the same with your own company. Pick any system your team uses and ask which door the data sits behind: your customers', your partners', or your own work. The customer door and the partner door stay closed. Your own work is the door we look at, and an afternoon with your team can be enough to sort a first list. Write down which door each system belongs to, and you will see your position before any AI lab asks.
Once your list is ready, I take it from there with you. I show a de-identified sample to several AI companies and AI labs, bring back their offers so you can compare them, and you make the final call. On the data side, I help your team choose the exports and the file format. Contracts and insurance are between you and the AI lab and depend on the deal, so we start only after your team signs off.
Whether your company moves money or cares for patients, the customer data stays locked, and the work behind the product is what an AI lab may want to read.
If you run a company with 30 or more people, the data you already have could become a new revenue stream.
Want to find out?
Try our data value calculator at AITrainingDataBroker.com: it gives you an estimate, not a final offer.
If you have further questions, leave them in the comments, and I will answer them in the next video.
Thank you for watching.
I will see you next time.
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