Transaction monitoring

A transaction monitoring system identifies higher-risk transactions (art. 20 para. 2 AMLO-FINMA). Statisticians give such tasks the respectful label of ‘ill-posed problems’, as they cannot always be conclusively solved. Statisticians love solving these problems. They frequently begin by trying the indicator method, which identifies indicators of risk. The more indicators that are identified, the more likely it is that the transaction is high-risk. Machine learning is used to calculate how to combine and weight indicators.

The basic method used does not significantly differ between traditional and crypto transactions. Both seek to solve the same fundamental problems:

  • What information is available for calculating indicator values? 
  • Which indicators correlate with risk? 
  • Are the indicators dependent on each other, making a simple combination difficult? 
  • Which machine learning method is most appropriate? 
  • Is there sufficient training data to allow the chosen learning method to optimize the risk calculation and reduce errors to an acceptable level?

Complete Revision of the Federal Data Protection Act

Complete Revision of the Federal Data Protection Act: „As of 15th September 2017, draft and report for a completely revised Federal Data Protection Act is public. In a first step parliament and the people agreed to adaptations in order to be compliant with EU law. The second part of the revision is debated by the parliament since September 2019. Data Protection is to be increased by giving people more control over their private data as well as reinforcing transparency regarding the handling of confidential data.”


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