FinScan’s data quality engine cleanses your data before screening begins
How does FinScan reduce false positives?
Built-in Data Quality Engine + Transparent Matching Technology
FinScan Data Quality Engine
Cleaner Data = Higher Matching/Screening Accuracy
Enhance the accuracy of your matches so that you can focus on the real risks. FinScan’s Data Quality Engine helps you:
- Standardize your data and fix name and address errors
- De-duplicate records to enhance efficiency when clearing alerts
- Identify and screen the true names even when they are hidden in the wrong fields
- Create compliance-ready data that enables your program’s success
FinScan makes any data compliance-ready for the most accurate screening results,
recognizing hidden names and identifying potential true hits.
Both Al-Rahman Welfare and James McClintock are identified as true hits in FinScan, while other screening systems would have missed them.
FinScan is able to prevent false positives and the risk of missing true hits by correcting data issues. Many compliance departments must rely on data quality procedures at the organization’s core system level, but those systems typically do not deliver data fit for compliance screening.
Data Errors FinScan Identifies and Addresses to Prevent Missed Hits and False Positives
Actual example of a FinScan customer data analysis
FinScan Matching Technology
Granularity and Transparency for More Accurate, Explainable Results
Understand the reasons why records match — at a granular level. FinScan’s Matching Technology helps you:
- Determine match quality by the context of differences
- Easily explain your algorithm-based decisions to regulators
- Configure your matching rules at a “granular” level to pinpoint the exact matches you want to review, e.g., apply different matching rules by risk level, internal data source, and compliance list, to enable a risk-based approach
- Minimize iterations in fine tuning your rules
- Stay focused by generating only high-quality matches
- Reduce false positives without increasing the risk of missing a true hit
Transparency means efficiency and being regulator-ready
- In a traditional percent-based matching algorithm (also called weighted scoring system), can you distinguish between one 85% match and another 85% match?
- Two match results could have the same score, but if you dig deeper, each hit could have been generated for very different reasons
- FinScan’s matching algorithm provides match results in terms of patterns instead of an aggregate score so you can know with confidence why the matches came together
- FinScan delivers a granular and transparent match result that expedites your clearing of the hits and makes it easier to explain your decisions to regulators
*Premium is a bundle of software modules from Innovative Systems’ FinScan and Enlighten® product lines.