FinScan® | Data Prep

Is Your Data Fit for Compliance Screening?

Front-end your existing screening process with FinScan Data Prep to drastically reduce your false positives and risk by 50% or more.

The issues of risk mitigation and false positives become further complicated by data problems that emerge due to poor quality data. It not only lowers analysts’ productivity, but also jeopardizes the business and poses reputational and financial risk. Improving the quality of your input data and getting it compliance-ready will significantly increase the accuracy of your screening results. You will get higher quality matches and fewer false positives, which will allow you to focus on the real risks.

A new level of accuracy, efficiency, and risk mitigation

FinScan Data Prep is a next generation compliance data preparation solution that effectively addresses the industry’s number one issue – false positives. By combining FinScan’s powerful analysis, cleansing and matching capabilities that have been developed over 50 years of processing customer data, FinScan Data Prep is uniquely positioned to fix internal data issues that can derail your compliance efforts. There’s no need to replace your existing system – FinScan Data Prep goes to work at the front end of your current screening process to improve the quality of your data and drastically reduce false positives and risk.

FinScan Data Quality Technology

Why FinScan Data Prep?

Compliance professionals have no control over the quality of the input data they use for screening, yet are responsible for the consequences resulting from bad data – namely, too many false positives, unnecessary due diligence, and the ultimate risk of missing a true hit, which can lead to huge fines and reputational damage. FinScan Data Prep cleans and de-duplicates your data before screening begins, so you get better results.

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Make your data reliable, consistent, and complete for optimal compliance screening

Data Profiling Icon


Create a complete overview of the data to identify format variations and incomplete and missing information in name, address, Date of Birth, ID, or any other field critical for screening accuracy


  • Ensure the integrity of your data
  • Understand gaps in data collection during client onboarding
  • Effectively set up matching rules to reduce false positives
Data Profile Example
Data Cleansing Icon


Improve the quality of the data for compliance screening by parsing and standardizing incomplete, inaccurate, incorrect, or irrelevant data fields; ignoring data cleansing is detrimental and can lead to high false positive rates and the risk of missing a true hit


  • Identify hidden names in joint accounts and address lines to uncover potential sanctioned entities
  • Flag dummy data and noise words so they don’t lead to false positives and unnecessary review efforts
  • Parse address elements into the appropriate fields to identify sanctioned countries hidden in address lines
Data Cleanse Example
Deduplication Icon


Identify and remove duplicate records that feed into the compliance system, in spite of multiple data capture sources


  • Deduplicating records reduces the number of alerts, which improves review time productivity by an average of 30% (as the compliance analyst does not have to review redundant alerts) and leads to the most optimal use of compliance resources and budget
Deduplicate Data Example

Read more about how good quality data can improve the effectiveness of your AML compliance screening.


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