Assumed risk versus actual risk: The new world of behavior-based risk modeling

Viridiana Lourdes explains how banks and financial enterprises can adopt and integrate actual risk models with existing systems to enhance the performance and operational efficiency of the financial crimes organization. Join in to learn how actual risk models can reduce segmentation noise, utilize unlabeled transactional data, and spot unusual behavior more effectively.

Over the last few years, financial institutions have dedicated significant time and effort to identifying how data science and artificial intelligence can help them better understand customer behavior, combat financial crimes, and regulate anti-money laundering. However, the traditional “assumed risk” models they’ve been deploying are only leveraging KYC (know your customer) data and data that customers voluntarily disclose about themselves. While still valuable, “assumed risk” models only reveal the tip of the iceberg. What financial institutions need to adopt is an “actual risk” model—a data analysis model that considers customers’ transactional data and real-time behavior that can be used to track financial crimes and predict future behavior. Viridiana Lourdes explains how AI/ML professionals and data scientists at financial institutions can adopt and integrate actual risk models with existing systems to enhance the performance and operational efficiency of the financial crimes organization. Join in to learn how actual risk models can reduce segmentation noise, utilize unlabeled transactional data, and spot unusual behavior more effectively.