Compliance
Customer Screening
How Financial Institutions Can
Reduce False Positives WithIntelligent MatchingAs AML compliance requirements
grow, so do the number of alerts thatmust be manually researched. This
makes weeding out false positives
increasingly necessary.
To be in compliance with Anti-Money Laundering (AML) andCounter-Terrorism Financing (CTF) processes, banks andfinancial institutions must screen customers and transactions
against various lists. As those lists and compliance obligations
have grown, so too has the number of matches or near-matches
that must be manually investigated. The investigations canbe complicated and time-consuming, and the vast majorityof alerts prove to be false positive. Financial institutions
with millions of customers to screen can find the costs ofcompliance oppressive.
LexisNexis® Intelligent Match Decision Solution is built tosolve this false positive problem—click here to learn more
about how it enhances LexisNexis® Bridger Insight® XG.

2

Automated matches generate alerts
Know-Your-Customer (KYC) screening has been a key partof AML and CTF processes in the financial services
industry for many years. Customer details arescreened against a variety of complianceintelligence information, including
sanctions lists and Politically Exposed
Person (PEP) databases. A variety ofcharacteristics such as name, date
NAMEof birth, nationality, domicile,occupation, etc. make up an
institution’s data profiles ofcustomers and other banking
counterparties, and of theindividuals and organizations
NATIONALITY
recorded in complianceintelligence databases.

DATE OF BIRTHDOMICILE

OCCUPATIONPEP DATABASESANCTIONS LIST3

An automated match between acustomer profile and a complianceintelligence profile generates an alert
to be manually reviewed. KYC screening is carriedout initially as part of the onboarding process andis repeated throughout the customer’s relationshipwith the institution. Rescreening might be triggeredby changes to the customer data or intelligence
information. It may also be performed periodicallyin accordance with AML risk management policies.

This initial and ongoing screening is aconstant, resource-intensive process,requiring sophisticated systems to carryout automated matching as well as
significant human resources to review
the system-generated alerts. In an era inwhich the scope of AML control activities
is constantly expanding, process
efficiency has become a key objective
for most compliance departments.

4Many organizations find their current manual
review of alerts to be disproportionately
costly as compared to the perceived riskmanagement benefits of the overall screening
process. As a result, there is increasingpressure on AML system managers to
reduce false positive results—those alerts
generated by the system that are ultimately
discarded during the manual review process
because of the absence of a true financialcrime risk.

?
FALSE POSITIVE

Learn how LexisNexis Intelligent Match
Decision Solution can help you meetincreasing compliance demands andprotect your organization from regulatory
exposure without draining resourcesfrom your core business activities.
5In addition to tying up valuable
compliance resources, false positivealerts may also disadvantage innocentparties, whose financial activities may be
disrupted while false positive matches areinvestigated. Excessive volumes of false
positive results may even contribute to AML
risk. Staff members reviewing the alerts may
be more likely to overlook a true match
while working through a sea of false
positive alerts.
Organizations facingthese challenges ask:
What factors contribute to
the generation of false positiveresults? And how can thesefactors be managed to
improve the effectivenessof AML screening?

6

Approach tofalse positive reduction
The following considerations should be
kept in mind when attempting to reduce
false positive results.

vs.

Invalid vs. Valid
False Positive Results
The distinction between invalid and valid
false positive results can’t be overlooked
when attempting to reduce overall false
positive results. Such exercises frequently
focus on invalid false positive results, butefficiency gains can be greatest if bothcategories are considered.

Invalid false positive results
are those in which the customer
records and complianceintelligence details, as presented to thematching engine, do not suggest that thetwo profiles matched in an alert relate to
the same party. They represent a failure of
the matching logic.
The failure may be due to “fuzzy” matchingalgorithms that matched two names thatare not in fact similar, or because othercharacteristics indicate that the profiles
belong to two distinct parties, such as one
in which the dates of birth vary significantly,
or in which, for example, one party is ateenage customer in one country and theother is a senior politician in another.
Addressing rates of invalid false positives
requires a careful assessment of the matching
technology used and its configuration toensure any changes made do not haveunintended negative consequences.

7

Valid false positive results are those in which
the details, as presented to the matching engine,
indicate that they may relate to the same party, butupon investigation are found to be unrelated. This situation
typically occurs because key characteristics such as name andapproximate ages of two distinct parties are similar. While thenumber of common names in use around the world certainlypresents a challenge, some gains may be made by ensuringthat the populations being screened against each other do
not contain redundant or obsolete profiles.
Other matches that are later found to be unrelated may be theresult of sub-optimal data quality or structure that prevents thematching engine from identifying characteristics that distinguishthe two parties. The latter can be addressed by examining
the underlying flaws in the data being screened (either on thecustomer side or on the compliance intelligence side).

Find out how LexisNexis Intelligent Match Decision
Solution can help identify and clear false positives.

8

Risk Appetite
Throughout any efficiency drive, a reduction
in hit rates should not be the only objective.
All organizations should have a clearly defined
screening tolerance standard, approvedby senior management, from which those
responsible for configurative changes can work.

This standard should dictate the types of profile
match that management expects to generate—ascreening alert. It should also outline the kind ofmatches management does not want forwarded
to the manual review process. Systems managers
are then free to adjust the precision of matchingalgorithms within the limits set by the screening
tolerance standard.

9Documentation
Throughout any exercise to improve the efficiency
of screening programs, risk-based justification
should be clearly documented for all configurativechanges. Projects to reduce false positive alerts
are usually initiated as a resultof operational, budgetaryor regulatory concerns soconclusions tend to be
driven and documented
from the point of view ofresource implications.

LexisNexis Intelligent
Match Decision
Solution is built to help
solve this false positiveproblem—click here tolearn more.

However, the risk of increasing false negative
results (dropping true alerts) should also be
considered part of the exercise. At best, it may
result in negative reviews from regulators
or auditors, and at worst, it may lead torelevant matches being dropped from thescreening process.

10Intelligent Match Decision Solution is a fully integrated tool within
LexisNexis® Bridger Insight® XG that enables better, more sophisticateddecisions defined at the attribute level, yielding a higher percentage ofautomatically remediated matches.
For more information, visit risk.lexisnexis.com/global/en/products/
intelligent-match-decision-solution

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efficiencies and enhancing profitability.

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