Collaborative fraud detection

Multi-Party Computing

This is a call to Join the running MPC use cases on Fraud Detection and Combatting Poverty, or join the ideation on Secure, distributed learning.

Within Techruption, we investigate applications of technologies for privacy-proof data analytics: Multi-Party Computation (MPC), and related techniques like federated learning, enable organizations to jointly analyze (and learn from) their sensitive data without having to share or reveal this data. See for more information. Below more information on one of the two running use cases (collaborative fraud detection and combatting poverty) and an upcoming use case on secure, distributed machine learning. For all three, also new partners are welcome!

Why and what?

Often, insurance fraud by healthcare providers is discovered (too) late by health insurance companies, because they lack crucial information known to other parties like banks and the Chamber of Commerce (KvK). Combining fraud indicators may give sufficient cause to suspect and investigate fraud, but this data cannot be readily shared between these parties due to confidentiality of the data. MPC offers a solution to securely combine fraud indicators, without revealing the actual confidential indicators. This allows the participating parties to identify which companies need to be investigated further. A Proof-of-Concept has been developed to demonstrate the MPC solution. Also, this solution has potential for detection of other forms of fraud where information needs to be combined.

In 2020, TNO, CZ and Rabobank continue with this use case, investigating three tracks:

  • Technical: run and test the developed MPC solutions on different locations in the Netherlands.
  • Juridical/compliance: What data can be used? Can an investigation be started based on the outcome of MPC?
  • Functional: What fraud indicators and analyses on these indicators give useful information?


This use case is interesting for organizations with interest in learning how to share fraud indicators with other organizations in a secure way. Particularly: financial sector, health care sector, government/police.

Interested? Contact Pieter Custers for more info: