Built-in Data Quality Engine

A built-in data quality engine that prepares your data for optimal screening results

FinScan Premium+ makes your data compliance-ready by cleaning your input data to increase the accuracy of your screening results and reduce review time.


Reduce false positives without increasing the risk of missing a true hit

Get higher quality matches and fewer false positives so you can focus on the real risks.

Reduce false positives

Poor data quality is the main reason for high false positives. Prevent meaningless alerts by addressing data anomalies such as inconsistent formats, dummy data, spelling variations, and customer names misplaced in non-name fields.

Minimize risk

Inaccurate data parsing of poorly structured data leads to missing real hits. Identify and screen the true customer and entity names even when multiple names are included in the same field or improperly placed in address lines.

Enhance productivity

Screening messy customer data against compliance lists is challenging and results in unnecessary review of the same records over and over. De-duplicate AML alerts to clear hits more efficiently and reduce personnel costs.

Integrated data quality workflow

AML matching algorithms cannot process poor quality data. The data quality engine is part of the screening workflow so there is no additional work required from your IT teams.

How it works

Cleanse and deduplicate your data before reviewing alerts

Data cleansing

The cleanse module prepares both customer and AML list data by parsing and standardizing incomplete and inaccurate data including dropping noise words, flagging dummy data, and identifying true entities in joint accounts or misplaced fields such as address lines.

Matching process

Configure your matching rules by risk level, internal data source, and compliance list to pinpoint the exact matches you want to review, creating a highly-granular risk-based approach and minimizing false positives.

Alert deduplication

Identify and remove duplicate AML alerts in spite of misspellings or name and address variations to avoid unnecessary manual investigation and review of redundant alerts.

Case management

Enjoy quick adjudication based on granular and transparent match results that expedite alert remediation and make it easier to explain your decisions to regulators .

Case study

Property and casualty insurance company

The organization had concerns regarding high volumes of false positives and inefficiencies in the case review process. Their analyst team often reported looking at the same alerts more than once. However, their primary concern was the risk of missing true suspicious activity due to errors in the data.

Challenge 1: Missing data

2.4MM individual records were missing Date of Birth (DoB), and the matching criteria heavily relied on exact DoB to identify matches.


Reliance on DoB matching to identify AML alerts was relaxed.

Challenge 2: Duplicate records

20% of the customer records used for screening were duplicates.


AML alert de-deduplication resulted in 17K fewer matches, saving 2,796 man-hours of wasted review time.

Challenge 3: Formatting issues

85K customer and entity records contained misplaced, missing, and hidden names in non-name fields.


Misplaced names were identified and screened, and 407 exact OFAC SDN matches were uncovered.

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