Why do banks and financial service providers need synthetic data?
Cumbersome legacy systems, tightening regulations, growing security concerns, the inability to move, share and scale data to drive data-centricity and innovation are familiar problems in the financial services sector. The winners will be able to create a competitive advantage by solving those challenges with synthetic data.
How can synthetic data help?
Banks need to leverage flexible, discardable, and most of all privacy-compliant synthetic data products to serve a variety of internal business functions. Synthetic data is not personal data and is free to use, share and hold.
FINANCIAL SYNTHETIC DATA Use cases
Improve model performance by 2-15%
Your fraud detection models only perform their best when they are trained on the right data. Synthetic data helps to create balanced versions of your production data that provide more granularity and depth and that make it easier for fraud detection algorithms to pick up patterns.
Speed up POCs by 70% and reduce provisioning cost
Software solutions only come to life when running on great data. Many external vendors rely on your data to deliver their services. Provide vendors ready-to-share, privacy-compliant synthetic data sandboxes to test different vendor solutions in a privacy-safe way.
Use realistic enterprise test data
Data is the blueprint for customer experiences. Your test data should be mirroring your production data without exposing it to a data breach. Synthetic test data is highly realistic, easy to generate and automatically mimicks your production data.