Why successful banks and insurance companies now rely on machine learning in compliance
The number of Suspicious Activity Reports (SARs) is increasing year on year, and the recent release of the FinCEN files reveals how banks and regulators alike are struggling to monitor the flow of money. New anti-money laundering laws, increasing cost pressures and a lack of personnel are additional challenges for banks and insurance companies. Compliance departments have to face up to these challenges and find new ways of:
- analyzing vast quantities of data more effectively
- detecting suspicious patterns more quickly
- identifying compliance risks at an earlier stage.
Machine learning can provide a clever solution. By combining data with the knowledge and expertise of compliance officers, it can quickly and accurately detect complex cases of money laundering.
Our whitepaper provides an overview of the latest SAR figures, new regulations, and key steps towards greater automation in Compliance. It also includes 10 lessons learned when integrating machine learning into compliance systems.
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