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Supervisory Technology (SupTech) is poised to effect a paradigm shift in the fight against financial crime. SupTech-based data architectures that combine advanced data transmission, storage, and analytical technologies have the potential to dramatically enhance the accuracy, efficiency, and predictive power of traditional AML approaches, or supplant them altogether. In addition to automating many of the manual tasks in traditional methods, such as painstakingly reconciling FIs’ suspicious activity reports, supervisors can now leverage Big Data applications to scrutinize the raw transactional and customer data themselves drawing on historical and real time data. Cutting down on preparation and travel time for data collection frees up time and resources for deeper off-site analysis and more frequent on-site inspections. Dynamic and interactive dashboards helps supervisors visualize data in novel ways and draw insights that might have been previously hidden, which also helps to make investigations more targeted. Similarly, innovative machine learning models allows regulators and supervisors to identify new patterns of suspicious activities not detectable by manual analysis. This case studies describes the CNBV’s prototype developed under the auspices of R2A, which demonstrated large efficiency gains that could be reaped from a full-scale roll-out of this solution, particularly in time saved from routine manual work. Data collection and processing that required weeks and days of manual work under the traditional approach can now be completed in minutes and seconds. New analytical models also delivered concrete results that supervisors can already use to improve anomaly detection. With more data mining and machine learning, these models are likely to uncover more illicit activity or even predict money laundering before it happens. |
