The goal of the project was to test the concept of using Artificial Intelligence to optimize the anti-money laundering process.
Comarch team performed an analysis of data sets containing millions of banking transactions, and then constructed an algorithm that assesses the risk of money laundering for each suspicious case.
The obtained results confirmed that the use of machine learning techniques can significantly accelerate the process of detecting money laundering, compared to the classical methods used today. For a financial institution this means a reduction of the time of suspicious case analysis by as much as 25%, and, as a result, a cost reduction of anti-money laundering process handling.
An important area that has also been examined is the possibility of generating hints explaining why a particular case has been classified as suspicious or rejected as a so-called false alarm. Such possibilities are provided by the Comarch Anti-Money Laundering system, equipped with the mechanism of explainable artificial intelligence XAI (eXplainable AI). This is important both from the point of view of the analytical work done by human employees and compliance with current regulations.
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