Data governance and regulations make it hard to get customer data at scale and on time.
Insurance companies miss out on customer experience, revenue streams, and competitive advantage without proper access.
Statice removes the traditional silos and compliance barriers to working with sensitive data by using synthetic data instead.
It's fast and safe to get data for data science use cases and customer analysis.
Traditional de-identification methods reduce data utility, leaving you with low-grade or insufficient results unsuited for machine learning or analysis.
Synthetic insurance 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.
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.
Exchange synthetic customer data internally and externally among local and international entities, enabling access to only 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.