The UN Office on Drugs and Crime estimates that 5% of global GDP is laundered yearly, with the digitization of the economy making fraudsters more difficult to catch. Artificial intelligence (AI) systems and machine learning (ML) models enable companies to get ahead against fraudsters by allowing them to gather and analyze massive datasets in real time. ML algorithms scan thousands of transactions, identifying hidden correlations or patterns, which is an impossible task for human risk analysts. Cloud technologies also play an important role in the latest anti-fraud developments, such as distributed cloud account protection. ML can also monitor login attempts, with companies using historical logs to train an algorithm on the most common user practices. As cyber-attacks continue to increase, real-time monitoring of internal systems and threat intelligence is vital for robust security.
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