Statice's synthetic data technology is now part of Anonos Data Embassy, the award-winning data security and privacy solution.
GLOBAL ENTERPRISES THAT ALREADY USE SYNTHETIC DATA
For financial institutions, leveraging customer data at the enterprise scale remains a challenge due to silos, regulations, and security concerns.
Diminished ability to examine customer data as a source of insights leads to a loss of competitive edge.
Financial synthetic data allows you to eliminate the traditional silos and compliance barriers of accessing and working with sensitive data.
Share customer data easily and safely to drive AI and Machine Learning activities, build better products and open up revenue opportunities.
Statistically sound
Traditional de-identification methods reduce data utility, leaving you with low-grade or insufficient results unsuited for machine learning or analysis.
Synthetic financial data offers a statistical value similar to the original data. It's fit to use as a drop-in replacement for your data science operations.
Demonstrably private
As organizations are unable to anticipate all secondary uses of the data, exhaustive consent collection isn't practical. Additionally, data masking can lead to fines and data disclosures if re-identification happens.
Using our privacy-preserving method, you can remove synthetic data from the scope of personal data regulations, simplifying the application of secondary data.
Fast to generate
Data should drive product development and fuel analysis, but instead, complicated and inefficient processes prevent teams from accessing, sharing, and leveraging it.
Synthetic data generates large volumes of data that can be used for BI, analysis, machine learning, easily accessed and shared.
Internally or externally share synthetic customer data across local and international entities, making it available to the relevant stakeholders.
Reduce compliance overhead by using highly representative synthetic data in your data science operations.
Improve your churn models or fraud detection systems by training your models with large volumes of synthetic claim or transaction data.
