Data Synthesis for Insurance | The Fake Data Spotlight Series

Abigail Sims
September 12, 2022
Data Synthesis for Insurance | The Fake Data Spotlight Series
In this article

    Data-driven industries like finance, healthcare, and insurance have been going through… a difficult time recently, to put it lightly. For insurance in particular, as data becomes larger and larger, the advent of malicious attackers and data leaks has led to even further regulation on how you can collect, store, and use data safely. 

    On one hand, it’s never been easier to use the data you have. On the other, regulations and software limitations still prevent innovative developers in insurance, at least, from getting the upper hand over their competition. Using production data is out of the question, and creating mock data for testing is time-consuming.

    Let's take a dive into the art and science of data synthesis for insurance companies, how it can help improve your organization, and why is the solution for you.

    How Data Synthesis For Insurance Is Shaking Up An Industry

    Few industries make use of data more than the insurance industry. Data is used for anything from risk assessment for analysts and underwriters, all the way to customer satisfaction and trend analysis. Newer startups in the insurance field have been shaking up the industry with new data-driven decisions, but the data has to come from somewhere. Possessing and analyzing the right data can either be a huge win or an incredible loss, so organizations can’t afford to play around with data.

    However, it’s tough for analysts and developers to get the data they need, thanks to the risks of using sensitive data. The imminent threat of a data leak can be a devastating event, and traversing the bureaucracy can be time-consuming to get the data your team needs.

    Leverage Your Real Data With Synthetic Data

    Using synthetic data can be a massive boon to your organization, but getting your hands on safe synthetic data can be a problem. It’s time-consuming to do it manually, and depending on your methods, it may still pose a risk of re-identification.

    With our AI Synthesizer, you have the power to take your existing data and create synthetic data that’s mathematically guaranteed to be impossible to be reverse-engineered to the original identity.

    Once you’ve got some grade-A synthetic data in your hands, you can accomplish a ton, such as:

    • Model and analyze your data. With Tonic, you can create subsets of data to perform advanced analytics without worrying about personally-identifiable information (PII). Your synthetic data acts as a stand-in, with the same information you’d need for your analytics without sacrificing security.
    • Lower costs and speed up development. Your teams don’t need to store bulky databases or spend ages trying to download and transfer data. Tonic’s platform allows your employees to cut those time-wasters out by automating and delivering it to exactly who needs it. Once that’s in place, your team can spend less time waiting for data and more time testing.
    • Innovate while keeping your investors happy. You don’t need to pick between appeasing your investors or creating new tools for your customers. With synthetic data, you can give your teams as much as they need to keep testing and innovating your products, while your investors can see the improved customer satisfaction from these new services.
    • Circumvent regulations. Nobody wants to make the mistake of leaking private customer data. Not only is it a massive invasion of privacy for your customers, but it can stick your company with a massive fine in the process. Thankfully, Tonic’s platform is compliant with all of the regulations you’d run into, giving you peace of mind when testing.

    You can take control of your data and how you use it, thanks to Through our platform, we take your already-existing data and mathematically guarantee that the synthetic data we generate can’t be re-identified, subsets can be created and shared across teams, generate accurate synthetic data, and so much more.

    No Need To Wait For A Rainy Day

    Insurance companies value data for just about everything, from risk analysis to underwriting. But using their existing production data, whether it’s for analytics or development, exposes them to even greater risks. Utilizing synthetic data can alleviate these issues and, better yet, improve your team’s workflows to boot.

    If you’re interested in getting started with using real fake data in your workplace, take a peek at our ebook The Subtle Art Of Giving A F*** About Data Privacy to learn how seriously we take protecting your data. (Spoiler: It’s a lot.)

    Abigail Sims
    As a reformed writer now deep in the marketing machine, Abigail can (and will) create narrative-driven content for any technical vertical. With five years of experience telling brand stories for tech startups and small businesses, she thrives at the intersection of complex data and creative communication.

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