Regulators require banks to fight against crimes like money-laundering, insider trading, and identity theft. They also expect banks to enforce sanctions. If banks do not comply, they can anticipate hefty fines. As a result, compliance departments of banks have sharpened their monitoring rules and are now generating an enormous number of alerts. Those alerts need to be handled and assessed by compliance agents, an increasingly impossible task given the amount of work they have. The currently deployed solutions are inapt to comply with regulations as proven by the $321 billion fines banks have paid since 2008. It is time to implement a new solution.

This white paper proposes banks to adopt a solution based on innovations in data management and state-of-the-art machine learning. A machine learning system learns from the experience and feedback of the compliance agent. As a result, the false positives and thus the total number of alerts to be investigated can be significantly reduced. This solution is much more sensitive than the old solutions when it comes to catching true positives as it combines and enriches the signals using data that transcends the account level. This means that criminal behavior is identified much earlier than with any of the current solutions. And on top of that, indicating risky and illegal behavior becomes a much more accurate process than ever before, allowing for very sophisticated models to prioritize subjective alerts.

With alert shaping, compliance work is made more efficient resulting in less time and money spent on false positive alerts. Compliance departments can intervene more effectively in cases of genuine positive alerts. The learning ability of artificial intelligence helps organizations to improve their responsiveness continuously. Being responsive makes those organizations better equipped to combat criminals and therefore to comply with regulatory rules.