Artificial Intelligence to improve Suspicious Transaction Reporting (STR)
Prioritas
Double the speed of Suspicious Transaction Reporting
Prioritas is the solution for prioritizing AML reports based on their likelihood of becoming true STRs
This supervised learning-based Machine Learning application was trained by learning from the financial institution’s previous evaluations of money laundering reports detected by traditional rule-based systems.
The objective of Prioritas is to bring the attention of the AML analyst to the truly relevant alerts as quickly as possible, thus enabling prioritized rather than random evaluation.
Reduction of reporting time
Prioritizing the workload reduces the time for reporting suspicious transactions by 60% to 80%
Better use of analyst’s effort
The occurrence of wrong predictions for low priority alert is close to zero.
Insight for the reduction of false positives
Analysis of low priority alerts provides valuable insights for improving the calibration of rule-based systems, enabling the reduction of false positives.
