When it comes to data in the finance industry, it’s everyone’s duty to keep sensitive information private and secure. However, developers need some form of data in order to develop and test their fintech software solutions.
But we can’t exactly use production data with a few creative redactions, can we? The development method of ‘move fast and break things’ doesn’t work in fintech… so how can you test safely?
Let’s look at how data synthesis in the realm of financial data can help alleviate these issues, and how Tonic.ai is the solution you need to fake your numbers right.
Flexible Data Sets That Meet Your Compliance Needs
With data synthesis for finance, you can create all the fake numbers you need while safely navigating the labyrinth of compliance and regulation. We give you a safe solution for your data needs, without risking the privacy of your customers.
With Tonic, you can generate safe data that looks just like your production data without any of the risks of compliance or privacy violations. With data synthesis, your team can safely create useable data for development and testing. And it works regardless of how complex your data is.
Data Synthesis for Finance = Safe Data
While it might look and act like your production data, it’s entirely synthetic and mimics production by using your existing data. We safely create data synthesis based on your existing data, and it’s free of any personal identifiable information.. Using data synthesis for finance can help your team stay compliant with regulations like GPRD and PCI.
- Innovate your workflow by keeping your data safe, and compliant with regulations. Your teams can start to scale and develop new features without worrying about red tape. Plus, generating and distributing a subset of mimicked data is lightning-quick, so your team will always be ready to test.
- Your data stays complex thanks to our stellar mimicking capabilities. With our powerful AI Synthesizer, your existing data is used to create data synthesis that remains statistically similar to your real data. This can even create event data that looks similar to your production data.
- Safely navigate privacy regulations with our PII/PHI-compliant data. When the platform creates data synthesis, our Privacy Scan flags any sensitive information for you to review. When you do, you can apply a generator to de-identify it. That way, your organization can stay confident during audits, and dramatically reduce the risk of a data leak.
- Differential privacy. Enough said. With the power of differential privacy, our platform generates data that is guaranteed to not reveal anything about a specific member of a data set. Instead, you’re only able to get broad information, such as transactions in a given zipcode. No amount of post-processing or additional knowledge will ever identify PII.
- State-of-the-art security features. We offer role-based access control, so you can give the right access to the right people. Admins can manage other users’ access to the platform, ensuring that only the right people have access to production data.
At Tonic.ai, we provide you with mimicked data that looks and acts like production, so that you can keep testing and developing without any compliance issues, or privacy concerns.
Don’t believe us? Check out our case study with Kin Insurance, where we had to work with them to protect their user’s data in a sensitive industry.
Closing the Books
Moving fast and breaking things doesn’t work in the finance world, but testing still needs to get done. Using data synthesis for finance can ensure that your data stays private, while still offering data synthesis that remains as complex as the source, all without needing to navigate the labyrinth of data compliance laws.
If you’re interested in getting started with using real fake data in your workplace, book a demo with us and see how it can fit into your workflow.