In today’s digital world, financial companies collect and process huge amounts of personal data, including banking habits, spending patterns and even the likelihood of repaying a loan. But how can this sensitive information be used responsibly, without putting privacy at risk?
That’s exactly the challenge the ENCRYPT project set out to solve. As part of its mission to bring privacy-preserving technologies to real-world industries, ENCRYPT took on a FinTech use case, focusing on secure data analytics in the financial sector.
Why Privacy Matters in FinTech
Whether it’s predicting loan defaults or improving debt collection strategies, financial institutions rely on data to make smarter decisions. But this data often includes highly sensitive details such as names, income levels, bank accounts and payment history.
In Europe, privacy laws like the General Data Protection Regulation (GDPR) strictly control how this kind of data can be used. That’s where ENCRYPT’s technology comes in – it helps financial companies gain insights from their data without ever exposing private information.
Smart Tools for Secure Analytics
To make this possible, ENCRYPT built a platform packed with cutting-edge privacy technologies. Here’s how it works in the FinTech world:
1. Recommendation Engine: Picking the Right Privacy Technology
Every financial task is different. Some are urgent, others need more accuracy and some involve huge datasets. The ENCRYPT platform includes an AI-powered tool that helps users choose the best privacy method for their needs. It considers things like:
- How sensitive the data is
- How big the dataset is
- How much time and computing power is available
This smart engine ensures that companies use the strongest possible protection, without slowing down their work.
2. Differential Privacy: Hiding in the Crowd
One powerful technique ENCRYPT uses is Differential Privacy. Imagine a method that lets you analyze data without ever identifying individual people. This is what Differential Privacy does. It adds “noise” to the data, making it difficult to trace any results back to a single person.
In the FinTech use case, ENCRYPT tested this by analyzing synthetic financial data. Even with privacy protections in place, machine learning models still achieved high accuracies.
3. Trusted Execution Environments (TEEs): Safe Spaces for Data
Sometimes, data needs to be processed in a special secure zone called a Trusted Execution Environment (TEE), which is a part of a computer where no outsider can peek in, not even the operating system. ENCRYPT used this to protect financial applications, offering stronger security guarantees.
4. Homomorphic Encryption: Calculations on Locked Data
Imagine math within a locked box! That’s the magic of Homomorphic Encryption. ENCRYPT used this advanced method to process encrypted data, without ever decrypting it! It’s especially useful when the data is extremely sensitive, like in credit scoring models.
Although this technique can be slow, ENCRYPT improved performance using optimization tricks and GPU acceleration, helping to make secure predictions faster and more practical.
Real-World Impact: Helping Banks Work Smarter
ENCRYPT partnered with financial companies to test these technologies in real-life scenarios. For example, EPIBANK managed customer data, while EXUS analyzed it to predict which customers might repay debts. Thanks to ENCRYPT, EXUS could train its models without ever seeing the original data.
This means banks can now:
- Make better decisions
- Keep customer information safe
- Comply with privacy regulations like GDPR.
Looking Ahead
The FinTech use case is just one example of how ENCRYPT’s privacy-first platform can transform industries. Whether it’s healthcare, finance, or beyond, ENCRYPT is proving that you don’t have to choose between data insights and data privacy, you can have both!