The best use of your data can only be achieved with excellent data management. Customer data in the insurance industry is sensitive and cannot be freely shared between departments or external partners, slowing down data analysis efforts.
Through testing Statice's synthetic data solution, the data science team at Provinzial, the second largest public insurance group in Germany, aimed to revamp the way they put their customer data to work.
Due to the challenges of sensitive data usage and the need to work with data faster in a competitive market, Provinzial sought out advanced data anonymization solutions. Provinzial used synthetic data for 'next best offer', a form of predictive analytics, to identify the needs of over a million customers.
The Provinzial data science team:
Anonymization methods like masking or k-anonymity can increase privacy, but at the expense of utility. Because Provinzial's customer data was highly detailed and extremely sensitive, they needed an anonymization solution which would not adversely impact the usefulness of this data.
Synthetic data turned out to be a great fit as it maintained the statistical value of original data, thus increasing the utility. The Utility Evaluator wraps multiple evaluators and provides a high-level view on the utility of our synthetic dataset without disclosing any of the statistical properties.
Provinzial's data team was seeking a high privacy-preserving solution to meet the GDPR requirements and company's internal privacy regulations to obtain approval for the use of sensitive customer data.
Synthetic data ensured high level of privacy. The process of generating synthetic data completely breaks 1-1 relationships between original and synthetic records, minimizing the chance of re-identification.
Statice solution added additional layers of privacy to the synthesization mechanisms, such as differential privacy.
For the Provinzial data science team, it was essential to be able to reduce time-to-data without having to change the internal system. The solution had to also go along with the existing workflow of the data without disruption.
The team established a data architecture using anonymized synthetic data and could perform specific tests without needing original data, resulting in accelerating time-to-data by 4 weeks.
Provinzial used their existing “next best offer” model (a form of personalized marketing based on predictive analytics; the next best offer model predicts consumers' needs and shows them offers and products based on their habits), to train it on synthetic data and compare the result to the model trained on real data.
Provinzial team performed a three-fold evaluation, focusing on data usability, model usage and privacy regulations.
"Statice's solution helped us conduct predictive analytics and test our hypotheses while keeping customer data secure. We have found it to be a useful solution for our data science team to simplify data access and focus on our data projects, machine learning model optimizations, and testing new ideas." Dr. Sören Erdweg, Artificial Intelligence & Data Development at Provinzial
Predictive analytics help insurers gain actionable insights into every aspect of their business, improve customer experience, increase sales, and look into the future. In order to deploy predictive analytics in a compliant and privacy-preserving manner, organizations will need to utilize data anonymization methods. For Provinzial, synthetic data proved to be the ideal solution. After all, data is only a strategic asset when you can put it to work.