The performance and effectiveness of screening engines are becoming under pressure due to rapidly changing and increasing regulatory demand, with risk detection capabilities of existing systems unable to keep pace. A typical symptom of poor screening efficiency is an ever-growing backlog of screening alerts and unsustainable levels of false positives, both factors having a direct impact on operational costs.
At the core of an effective screening solution is an uplift of the completeness and accuracy of the data being ingested by the screening engine. Tuning the matching and filtering performance of the screening engine requires the data to be complete and of high quality, ultimately resulting in a boost in true positives detection rates and operational efficiency.
In addition to ensuring the screening engine is operating at peak performance with accurate data, emerging AI and analytical methods can also be used to address operational efficiency issues related to case investigation.
Machine learning techniques can be coupled with predictive calculations based on historical investigator decisions to substantially lower the number of alerts to be safely dispositioned, so investigators can focus on those with the greatest likelihood of being true positives.
By helping to build processes that are more likely to result in complete and accurate data, and optimizing the engine to avoid false positives, the effort and cost involved can be reduced.