Improve screening results with better data

Stop wasting valuable resources on unnecessary alerts. With FinScan Data Prep, you can quickly and easily resolve data errors and inconsistencies to prepare your data for screening with minimal effort. Take control of your screening results today.

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Spot data anomalies 

Gain insight into the condition of your data, and detect any irregularities or incompleteness to make strategic decisions on the optimal configuration of your screening algorithm.

Improve screening results 

Streamline your screening process by eliminating data inconsistencies, standardizing name patterns, and removing duplicate records for better screening results.

Activate quickly 

Get up and running quickly. Simply turn on the Data Prep module in FinScan and start experiencing improved results right away.

Uncover hidden risk

Identify and screen all names in joint accounts and address lines separately so that no potential sanctioned individual or entity goes unchecked.

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Understand the state of your data

Start with a data quality assessment to pinpoint data irregularities and gain visibility into the quality of your data so you can effectively address the data errors that impact your screening results.

Identify inconsistencies 

Compare the differences in data standards between your customer data and the AML lists to determine the optimal format for cleansing data. Standardizing all data going into the screening process will give your matching algorithm increased accuracy.

Pinpoint gaps in your data 

Analyze the completeness of your data to determine which fields should be used as primary and secondary matching criteria; otherwise, you may encounter a high number of false positives and overlook true hits.

Spot duplicates

Identify duplicates within and across data sources to prevent multiple screenings and reviews of the same customer. Duplicates drastically increase false positives, as well as needlessly burden compliance teams with extra work.
Screening Optimization

Improve screening results quickly

Address data quality issues impacting screening outcomes to reduce false positives while uncovering the potential risk of false negatives, warding off expensive penalties and preserving your business’ reputation.

Automate the process of formatting all your data sources and AML lists with FinScan Data Prep after completing a thorough data quality assessment and selecting the optimal format for names, addresses, date of birth and any other field.

Optimize your screening configuration for each source application to gain higher flexibility and attain superior outcomes, as data quality varies from source to source. Ensure the matching criteria selected are consistent and complete.

Eliminate redundant review effort by letting FinScan Data Prep identify and link duplicates, only screening the parent record, and forwarding the screening results of linked duplicate records.

Improving data quality reveals hundreds of previously undetected OFAC hits

Screening Optimization

Data anomalies corrected by FinScan Data Prep

Leverage FinScan Data Prep to address the most common and challenging data quality issues that impede screening results. Some of them are listed below.

NAME ERRORS INPUT RECORD IMPACT OF NOT USING FINSCAN DATA PREP ACTION TAKEN WITH FINSCAN DATA PREP
Joint accounts

JOHN & MARY SMITH

Additional names in the same record, e.g., Mary Smith, are not screened

Detects additional names and creates separate records for screening:
1. JOHN SMITH
2. MARY SMITH

Names hidden in address lines

INNOVATIVE SYSTEMS, INC 123 MAIN STREET C/O JOHN SMITH

Additional names in address lines, e.g., John Smith, are not screened

Identifies name from address lines to screen them separately:
1. INNOVATIVE SYSTEMS, INC 123 MAIN STREET
2. JOHN SMITH 123 MAIN STREET

Reverse name order

MCADAMS JULIE

Assumes “Last Name” to be the “First Name” thereby leading to incorrect matching and creating false alerts

Reorders First Name and Last Name:
JULIE MCADAMS

Invalid address elements

Street: 205 TRENTON AVE CLIFTON NJ USA City: State: Country

Incorrect address capture leads to inaccurate matching and missing sanctioned countries, e.g., street line has the entire address line including city and country

Parses address fields into appropriate fields for better matching outcomes:
Street: 205 TRENTON AVE | City: CLIFTON | State: NJ |Country: USA

Format variations in DoB

DoB: 10-Apr-88 DoB: 4/10/88

Format variations in Date of Birth field leads to increase in false positives

Standardizes DoB formats to avoid variations:
DoB: 19881004
DoB: 19881004

Individual identified as organization or vice versa

BROWN MARKHAM LLP Type: Individual

Incorrect labeling of records can lead to missing true hits

Correctly identifies the record type to screen against the appropriate sanctions dataset:
BROWN MARKHAM LLP
Type: Organization

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