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Tengo curiosidad si alguien ha integrado con éxito la IA más allá de los sistemas básicos basados en reglas para la detección de AML en tiempo real, especialmente para flujos de pago de mayor volumen. ¿Cuáles han sido los principales obstáculos con los falsos positivos frente a la detección de amenazas reales?
We've experimented with some unsupervised learning models for anomaly detection, but the regulatory burden of explaining why something was flagged makes it challenging to implement at scale. False positives are still a significant drain on resources.
We've experimented with more advanced AI for real-time AML, and the biggest hurdle has definitely been tuning the models to reduce false positives without missing critical anomalies. It's a constant balancing act between sensitivity and specificity, particularly with the sheer volume of transactions you're talking about.
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