How bank compliance departments benefit from the explainability of machine learning decision-making processes

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Intelligent software helps banks and financial service providers to carry out their compliance tasks. The use of machine learning processes means that banks can analyze vast amounts of data with greater efficiency, uncover suspicious patterns and identify potential risks at an early stage.

Why does machine learning need to be more transparent?   

In its report Big Data Meets Artificial Intelligence, Germany’s financial supervisory and regulatory authority BaFin stresses the need for machine learning models to be explainable. They cannot simply be categorized as black boxes. So why should banks still rely on machine learning, and how can they make their compliance-related decisions more transparent?

Find out more about how banks and financial service providers benefit from improving the traceability of their machine learning processes. 

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"Our experience shows us that automation is only successful when machine learning is combined with input from compliance specialists. Only they can assess the data in accordance with their particular objectives, so compliance experts are needed to manage the process and increase efficiency."

Thomas Ohlemacher
Product Manager Compliance, ACTICO