Building upon AI and ML to Smooth Out AML: Key Trends, Perspectives, and Challenges

Building upon AI and ML to Smooth Out AML: Key Trends, Perspectives, and Challenges

This IDC Perspective illustrates trends and challenges that banks must cope with in managing anti-money laundering (AML) risks and in fighting financial crime activities. Corporate banks must fulfill mandatory regulatory requirements defined across multiple jurisdictions and that are evolving over time. Financial organizations must ensure that while supporting their corporate customers' businesses, they are undertaking all appropriate measures to verify that their customers' transactions do not entail fraudulent activities; this way, they protect the bank-self, the customer, and the whole financial system and preserve the industry's trustworthiness. To properly act as trust gatekeepers, banks must adopt robust risk management procedures and properly deploy resources in terms of people, processes, and technologies. Since the fight against financial crime is a moving target, financial organizations must ensure that they periodically review their frameworks to stay up to date with evolving best practices, and that they adopt the best available technologies. "Given the overwhelming amount of data to be processed along the AML process, banks must adopt solutions that leverage AI and ML technologies to achieve more efficient and accurate money laundering detection. Thanks to the these technologies, AML tools can evolve to counter criminal actors' creativity," said Maria Adele Di Comite, research director at IDC Financial Insights.

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