As artificial intelligence (AI) continues to push the boundaries of technological innovation, its potential to transform industries constrained by rigorous regulatory requirements is increasing. For instance, within the field of anti-money laundering (AML) compliance, a growing synergy is emerging between AI and human expertise. AI is revolutionizing AML compliance through efficiency and precision; however, human insight continues to be necessary to ensure effectiveness.
Keeping AI in Check
AML compliance centers on identifying patterns, anomalies, and subtle signals hidden within enormous and often complicated datasets. It is possible for AI models to have biases and fail to perceive subtleties that humans intuitively understand.
Consider the following scenario: a person from a country with a significant migrant population working abroad, generally in seasonal sectors such as agricultural labor, sends money to their family back home on a regular basis. However, remittances may temporarily cease, generally when seasonal work expires, and the migrant worker relocates to another geographic location for additional employment. This fluctuation in most cases is normal, but the system may flag these patterns of activity as unusual. The regularity and consistency of these transactions may look to AI as a divergence from conventional customer behavior, raising an alarm for further examination.
Humans on the other hand, with a cultural and market familiarity, understand the context. An analyst recognizes that these transactions are consistent with customer behavior. The individual’s status in this case is not indicative of money laundering or any other unlawful action. Instead, it reflects the common practice of international remittances.
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