For financial institutions, there’s a concerted effort to improve the effectiveness of anti-money laundering (AML) programs by incorporating automation into various processes. Despite these efforts, they are constrained by ineffective risk scoring and the resulting high false-positive rates. It’s crucial to understand that the accuracy of automated decisions heavily relies on the quality of the data they are built upon.
How exactly does multi-dimensional risk scoring work, and how can anti-money laundering professionals apply it to boost effectiveness and efficiency across their programs? Read more about the valuable insights from Quantifind’s CPO Adam Mulliken in an interview with an Oracle expert Jason Somrak: Learn More