Deal ticker: Kellerhals Carrard advises PostFinance on AI-supported transaction monitoring

PostFinance relies on an AI-supported solution for transaction monitoring in order to identify money laundering risks more efficiently and deploy compliance resources in a targeted manner. The machine learning application evaluates and prioritizes suspicious transactions, reduces false positives, and ensures compliance with money laundering regulations. The focus is on traceability, documentation, and ongoing validation.

Prof. Dr. iur. Cornelia Stengel, partner at Kellerhals Carrard and honorary professor at HSLU, supported the project together with Lea Ruckstuhl, counsel at Kellerhals Carrard.

The focus was laid on money laundering requirements and the current FINMA expectations regarding governance, risk classification, and data quality when using AI.

Frank Panknin, Head of Innovation Management Payment Solutions at PostFinance, says:

"For us, artificial intelligence is not an end in itself, but a tool for making our compliance more efficient and regulatory-compliant. Thanks to the excellent advice provided by Cornelia Stengel and her team, we were able to implement a secure and forward-looking solution. Their comprehensive expertise in financial market law, compliance, anti-money laundering (AML), and outsourcing played a decisive role in enabling us, as a systemically important bank, to fully fulfill our responsibilities."

Background information on PostFinance:

With the in-house development and productive introduction of an AI-supported solution to assist in the prevention of money laundering, PostFinance is the first bank in the German speaking region to set a new standard in dealing with regulatory requirements.

The application was developed entirely in-house and is based on state-of-the-art machine learning methods that were specially designed for use in a highly regulated environment.

The focus is not on automation for automation's sake, but on the targeted improvement of risk perception: The solution specifically reduces false positives and enables compliance teams to concentrate on the cases that are truly relevant.

The project was carried out on an interdisciplinary basis involving compliance, data science, and IT, and is a prime example of the responsible, transparent, and effective use of artificial intelligence in the fight against money laundering.