Learn how to overcome the privacy and data hurdles many of the world’s leading banks, insurances, healthcare providers, and telco organizations have faced. Are you looking for ways to scale AI within your organization? Or to get access to realistic and useful data for testing and digital product development? Or would you like to externally share data to collaborate with startups and vendors while ensuring GDPR and CCPA compliance? Whatever you want to accomplish with your customer data assets - if reconciling data-driven innovation and privacy protection is a priority in your enterprise data strategy, then synthetic data should be a part of it!

Curious to see what the world’s leading synthetic data platform can do for you? Visit https://mostly.ai/ and get in touch!

MOSTLY AI solves one of the biggest challenges organizations are facing today: balancing their need for AI & data-driven innovation with privacy protection and GDPR/CCPA compliance. Their synthetic data platform MOSTLY GENERATE helps organizations to unlock their privacy-sensitive big data assets by generating the world’s most accurate synthetic data for behavioral and time-series customer data (e.g. for financial transactions, insurance claims, healthcare data,...). MOSTLY AI’s fundamentally new approach to big data anonymization enables organizations to retain all of the valuable information in a dataset while protecting the privacy of each and every one of their customers. The result is completely anonymous data, that is free to use, free to share, and free to monetize.


As an enterprise, innovating with customer data is tough. Over the past few years, we've worked with some of the largest and most reputable brands in banking, insurance, healthcare, telco, and other industries. No matter what they were interested in, being it AI training, getting access to realistic training data, or external data sharing to collaborate with startups or vendors, they all faced the same challenges when it comes to utilizing the data. One of the challenges is that getting access to this data is super, super difficult and takes lots and lots of time.
We hear from our clients that, in the past, before they used synthetic data, it took them anything between three to eight months until they could get access to the relevant datasets for their projects. It's not only that. It's not the time and all the negotiations and all the bureaucracy your employees have to go through. In the end, if you get your dataset, the utility is not what you expect. The reason for that is that traditional anonymization techniques destroyed a vast majority of the utility in your dataset. If you want to learn more about this, we have a nice video that compares traditional anonymization techniques with synthetization.

So it takes a lot of time, it doesn't give you the utility you want, and on top of that, research has demonstrated over and over again that these traditionally anonymized datasets are easy to re-identify and put you at risk for privacy breaches and hefty privacy fines. Synthetic data, on the other hand, can help you to immediately get access to relevant datasets, to get access to granular datasets that are as good as real but completely privacy-friendly and compliant with GDPR, CCPA, and all the privacy regulations out there.
If you're wondering what synthetic data is, AI-generated synthetic data is pure value without liability. You can imagine it like this, instead of destroying certain parts of an existing dataset, like all these traditional anonymization techniques like masking and obfuscating do, you use a powerful AI algorithm that automatically learns all of the structures, the correlations, the time-dependencies, basically how your customers behave.
Then once this training is completed, a completely new separate synthetic dataset is generated that has the same correlations, the same patterns, the same time dependencies, but doesn't include any privacy-sensitive information of your real customers. This gives you access to a dataset that is statistically highly representative, super realistic, and yet 100% privacy-safe.
What can you do with synthetic data? One top use case we see with our clients is getting rid of the testing headaches. Organizations would love to use the production data for testing because it's the most realistic datasets they can get, but, of course, that's not privacy-friendly, that's not GDPR-compliant, and therefore, they have to find alternative solutions. Synthetic data allows them to get as good as real data that is free to share also in lower environments without having to fear about a privacy breach. But it's not only testing.
One of the biggest strengths of AI-generated synthetic data is the superior accuracy. It's the only anonymization technique out there that really produces data that's useful for AI training [...]