Machine Learning, through its algorithms, enables anticipating and building more accurate risk models with greater predictive capacity. However, the effectiveness of these automated systems depends on the quality of the analyzed data and the appropriate interpretation of the contexts, where human participation is essential to boost the fight against money laundering.
Currently, we have more precise tools that will provide decision-makers with a more comprehensive view to address this challenge. The future challenge lies in achieving an effective fusion between human judgment and automated learning without it becoming an obstacle to technological advancement.
The discussion on how to find that balance between AI and Machine Learning with the intervention of specialists from the financial sector is ongoing. This collaboration allows for understanding beyond algorithms and thoroughly evaluating the complex results that lead to crucial decision-making.
The Director of Anti-Money Laundering, Internal Control, and Data at Scotiabank Mexico emphasizes the importance of Artificial Intelligence (AI) in preventing money laundering risks in the financial sector. The ability of AI to process large volumes of data and recognize patterns through Machine Learning is crucial in this field.
Cybercrime and money laundering pose significant global challenges. It is estimated that between $800 billion and $2 trillion are laundered annually worldwide, despite legislative initiatives implemented to combat this practice. In this sense, technological innovation becomes increasingly imperative to strengthen current prevention mechanisms.
Currently, Artificial Intelligence and Machine Learning are allowing improvements in traditional controls, mitigating risks through algorithm analysis, and detecting behavior patterns among financial clients. In the banking sector, Know Your Customer (KYC) is essential in risk prevention, as it enables the identification of irregular financial activities through the assessment of transactions and banking habits.
KYC has become more effective thanks to the application of Artificial Intelligence and Machine Learning, as algorithms can perform predictive analyses based on historical data collected.
