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Privacy-preserving Machine Learning with die Mobiliar

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compliance with the e-FADP
ML model performance effectiveness
< 2 weeks
time to granular and compliant data


Die Mobiliar's churn prediction models utilized customer data, which was subject to evolving data protection laws. To maintain the company's ability to safely and efficiently use data, anticipating regulatory changes was essential. 

To future-proof their operations, die Mobiliar followed a compliance-first approach and incorporated privacy directly into their data processing activities with the help of synthetic data. In less than two weeks, die Mobiliar was able to produce and use highly granular, compliant data that would secure this aspect of their data operations in the future.

Die Mobiliar’s use of synthetic data proves that integrating compliance at the heart of digital transformation can future-proof the development of data-driven innovation for insurance companies.


Switzerland's first data protection law was enacted by the Federal Act on Data Protection (FADP) of 1992. In 2017, the FADP underwent a complete revision by the Federal Council to increase transparency, strengthen individual data rights, and align with European regulations. The revision of the FADP entered its final phase at the end of 2019. The fully revised DPA (E-FADP) is entered into force in 2021.

Besides strengthening privacy obligations, the new framework increased the requirements to document processing activities and implement governance processes. As a result of the new regulations, sanctions for non-compliance grew in scope and significance.

Die Mobiliar's churn prediction models initially used customer data, subject to changing laws regarding data protection. Keeping abreast of regulatory changes was crucial to maintaining the safety and efficiency of the company's data science division. 


Die Mobiliar used Statice's synthetic data solution to integrate privacy into its on-premise data processing. Data anonymization using synthetic data allows enterprises to protect consumer privacy and comply with personal data regulations. 


Synthetic data is artificially generated. Algorithms learn the statistical characteristics of an original dataset to create new data points that preserve the statistical properties and structure. Statice's technology comes with data utility and privacy evaluations - these features allowed die Mobiliar to get a clear picture of synthetic data effectiveness in a short amount of time.


In less than two weeks, die Mobiliar produced and used highly granular, compliant data that would future-proof their data operations and train their churn model without compromising on the model performance. In comparison to sensitive data, the model trained on synthetic data achieved 95% effectiveness.


Thanks to the privacy protection in place, die Mobiliar can use synthetic data for secondary purposes without needing to undergo lengthy compliance processes.

The Statice software protects the original data of our customers on the one hand, and on the other, enables us to work with the data across departments without compromising privacy or security."Georg Russ, Data Scientist, Data & Analytics at die Mobiliar
Case study


Die Mobiliar




Statice SDK

Goal:Establishing an effective and compliant method of processing customer data in anticipation of a new data legislation.

  • The data science team leveraged customer data to develop churn prediction models to improve customer retention.
  • The revision of the Swiss Federal Act on Data Protection (FADP) was expected to enter into force and impact the organization's ability to leverage data.
  • The team needed a solution that would maintain data granularity.

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