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THOMSON REUTERS WORLD-CHECK ONE

Document Version: 0.2 Issue Date: 26 Nov 2016

MATCHING OVERVIEW GUIDE

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Matching Overview Guide Document Version: 0.2

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Contents

Matching Overview Guide Document Version: 0.2

Contents

About this document........................................................................................................... 4

Intended readership ................................................................................................................................. 4

In this document ...................................................................................................................................... 4

Feedback ................................................................................................................................................. 4

Matching Names .................................................................................................................. 5

Matching Engine Key Features ............................................................................................................... 5

Increase Precision – Controlling Matching Results ................................................................................. 6 Minimum Score Threshold ....................................................................................................................................... 6 Include Native Aliases ............................................................................................................................................. 8 Exclude Low Quality Aliases.................................................................................................................................. 10 Auto Resolution Settings ....................................................................................................................................... 11 Other Controls ....................................................................................................................................................... 14

How to Screen Names as Last Name – First Name ..........................................................17

Frequently Asked Questions .............................................................................................18

Appendix A – Name Variation Examples ..........................................................................20

Appendix B – Stop Word Examples ..................................................................................21

Organizations ........................................................................................................................................ 21

Individuals .............................................................................................................................................. 21

Appendix C – Languages ...................................................................................................22

About this document

Matching Overview Guide of 23 Document Version: 0.2

About this document

Intended readership

Users of World-Check One, who are responsible for configuring the matching options available in World-Check One or using the platform to screen business relationship names.

In this document

World-Check One simplifies and accelerates the customer due diligence process.

The highly scalable solution is built for single users or large teams to support a highly targeted approach for screening during KYC on-boarding, ongoing monitoring, and rescreening cycles.

It makes remediation quicker and more intelligent and is adaptable to meet changes in regulation.

Part of Thomson Reuters, the unified platform built to meet the needs of the governance, risk and compliance community.

The software is built to maximize Thomson Reuters proprietary World-Check data, using:

Advanced name matching algorithms

Secondary matching

Filtering

Customizable workflow

Case management

Advanced administrative and reporting capability

Batch uploads, and

Ongoing monitoring for high volume client names.

This document gives an overview of the Matching Engine technology and methods to control matching results available in World-Check One for matching Names to World-Check profiles.

You can learn how to control the matching process in World-Check One to better suit your organizational requirements, lower false positives and increase precision and efficiency.

Feedback

If you have any comments on this document please contact the Thomson Reuters Risk Documentation team.

Matching Names


Matching Names

The matching technology (aka matching engine) used by World-Check One is provided by a proven third party supplier. This name-matching technology is integrated into government applications for matching names against watch lists; boosting the accuracy of identity resolution systems; and performing sophisticated name searches.

Examples of other organizations using this third party supplier technology are: Governments around the world and agencies of the U.S. intelligence community, the Department of Defence and the FBI as well as corporate customers like Oracle, Google and Amazon.

The text analytics and name technologies serve vital functions in monitoring and sifting open source intelligence; correlating names against watch lists. The advanced name extraction, normalizing, and matching software can be integrated into existing systems or be used as the basis for new systems seeking precision name matching in high-transaction environments.

In addition to the advanced matching technology, World-Check One also offers the ability to control the results of the matching process, thus giving you the control of reducing false positives, increase the precision of your matching and focus on the relevance of the due diligence task.

The control is achieved through the following key World-Check One features:

Matching Engine thresholds

Aliases

Secondary Matching (Auto-resolution Settings)

All the points above are discussed in dedicated sections in this document.

Matching Engine Key Features

Feature Description

Handles Common and Unusual Types of Name Variations

It removes the obstacles to performing record linkage and data alignment, including name variations due to nicknames, initials, missing name components, missing spaces between names, out-of-order name components, and the same name represented in different languages, and more.

Refer to Appendix A – Name Variation Examples for further

information.

Statistical Algorithms Recognize Countless Name Spelling Permutations and Names in Different Languages

The matching engine uses statistical algorithms to match names rather than relying on a long search through millions of name variations which can never cover every possible spelling permutation. Besides handling “Robert” vs. “Bob,” the matching engine also accurately matches “Mao Tse-Tung”, “Mao Zedong”, and “毛泽东” (simplified Chinese).

Translation of a name in non-Latin script to its Standard English form in Latin script.

Transliteration between non-Latin script and Latin script.

Orthographic enhancement (vocalization of Arabic names and segmentation of Chinese names).

A list of variant Latin-script representations of a name in non-Latin script.

Matching Names


Feature Description

Score A double greater than zero and less than or equal to one that indicates how similar the two names are. A value of 1.0 is returned if and only if the two name strings are identical. A score of less than 1.0 is returned for names that potentially match as indicated in Name Variations. As an example for the Arabic name نائف أبو شارخ and its IC transliteration Nayif Abu-Sharakh, this comparison returns a score of 0.99.

A score weighting is also included in the score calculation, based, among others, on statics.

For example a middle initial J. is statistically more common than a middle initial Z. so a name with middle initial J will be assigned a score higher than middle initial Z in case First Name and Last Name are a perfect match e.g. John J Smith and John Z Smith.

Stop Patterns A stop pattern is a regular expression that excludes matching name elements (like stop words) during the matching process. Most punctuation is stripped, with the exception of periods, commas, and hyphens; whitespace is reduced to single spaces; and characters are lowercased.

Elements in the names to be processed that match any of these regular expressions are removed. For example, ‘mayor’ at the beginning of a lowercased name and followed by whitespace is to be removed.

Refer to Appendix B – Stop Word Examples for further

information.

Increase Precision – Controlling Matching Results

Minimum Score Threshold

In World-Check One, minimum score threshold allows you to control the results set returned by the matching engine. Since the matching engine attributes a score to the World-Check results matching the Screening Name, you can control what results comprised in a range of scores you would like to be able to review and match.

The score can be comprised between weak (lowest) to exact (highest).

There are a number of different factors you will need to evaluate when choosing an optimum setting. One of those will be the quality of the data you are supplying for searching. If the quality of the data is good and there is confidence that all name elements have been captured then a tighter match setting should be more appropriate.

Your organization guidelines in terms of due diligence will also need to be considered when setting up the minimum thresholds. Depending on the organization, there could be stricter rules in place that would instigate setting the minimum threshold to Fuzzy (lower end) in order to be absolutely thorough in the checks and include in your review all possible false positives.

World-Check One, however, also offers other options to lower the incidence of false positives that are discussed later in the document.

You can set the minimum score for Initial as well as Ongoing Screening to following settings:

Fuzzy – will return matches with scores from weak to exact

Medium – will return matches with scores from medium to exact

Matching Names


Strong – will return matches from strong to exact

Exact – will only return exact matches

The Minimum score threshold can be controlled for the Client record (Primary Group) i.e. your organization and individually for each Group belonging to the client by accessing World-Check One Admin and then World-Check Screening.

Click Edit to display Group Settings page, which contains minimum score threshold settings, initial screening and ongoing screening:

EXAMPLE:

Name to be screened Mary Jane Smith

If you have set the Minimum Score Threshold for Initial Screening from Fuzzy to Exact:

The results shown will be including matches with Match Strength between weak and exact:

If you change the Initial Screening Minimum Score Threshold to be comprised between Medium and Exact:

Matching Names


The results will have the Match Strength between Medium and Exact, therefore considerably reducing the amount of returned matches:

Include Native Aliases

You have the ability to include native aliases that could potentially match the Name you would like to screen against World-Check profiles (subscription level: Premium Plus).

If you are Screening Names not in English or in general non Latin Script and you have turned ON the Include Native Aliases option , the system will return any World-Check profile that has native alias(es) matching the name you have entered on the Screening page.

The option to Include Native Aliases can be controlled for the Client record (Primary Group) i.e. your organization and individually for each Group belonging to the client by accessing World-Check One:

Admin and then World-Check Screening

Click Edit to display the Group Settings page, which will include the following option:

Matching Names


EXAMPLE

Name to be screened كتائب شهداء األقصى‎. The Include Native Aliases option is turned OFF for the group you are assigned to.

The results of the screening process will return no matches that are in fact based on Native Aliases:

Once you turn the Include Native Aliases option ON.

and screen forكتائب شهداء األقصى‎, the following matches are shown:

Matching Names


Exclude Low Quality Aliases

You have the ability to exclude low quality aliases that could potentially match the Name you would like to screen against World-Check profiles that may have low quality aliases (subscription level: Premium and Premium Plus).

The option to Exclude Low Quality Aliases can be controlled for the Client record (Primary Group) i.e. your organization and individually for each Group belonging to the client by accessing World-Check One:

Admin and then World-Check Screening.

Click Edit to display the Group Settings page, which will include the following option:

EXAMPLE

Name to be screened: Ann Haylwood. The Exclude Low Quality Aliases option is turned OFF for the group you are assigned to.

The results of the screening process will return two matches that are in fact based on Low Quality Aliases:

Matching Names


Once you turn the Exclude Low Quality Aliases option ON.

and screen for Ann Haylwood, the match cases on Low Quality Aliases are no longer shown:

Auto Resolution Settings

Authorised users can set rules for the World-Check One matching engine so that the system will auto resolve false positives returned when a User screens an Individual, Organizations or Vessels. The system allows also excluding those potential false positives entirely from the screening results.

The option to Auto-Resolve as False or Exclude from Results can be controlled for the Client record (Primary Group) i.e. your organization and individually for each Group belonging to the client by accessing World-Check One.

For Zero Footprint Screening the Auto-Resolve as False is not applicable:

Matching Names


From the Admin page in CLIENT CUSTOMIZATIONS, click edit Auto-Resolution Settings.

The following is displayed:

You can decide what secondary matching fields to use against what sources and what behaviour the system will follow with the World-Check Profiles not matching.

EXAMPLE

The matching Engine threshold is set Fuzzy to Exact. All secondary matching fields to be used for all PEP sources with Date of Birth within +/- 2 years of the date entered by the User in the Screening page and Auto-resolve as False.

Matching Names


When Screening Tony Blair, born in 1970 in Australia and resident in Australia, the system will auto-resolve as false any PEP World-Check profile not matching the information entered i.e. Tony Blair ex UK Prime Minister will be auto-resolve as a false match:

Name to be screened Tony Blair.

Matched automatically resolved by World-Check One as False.

Only matches shown to be resolved are the ones not matching the secondary matching rules.

Matching Names


Other Controls

Sources

You can also control the results of the Screening process by determining what sources you would like to screen against are.

By default all Sources (Sanctions, Law Enforcement, PEP, Regulatory Enforcement, Other Bodies-World/Check Research) are set ON, but you can turn OFF an entire source type (e.g. PEP) or selectively sub-groups within a Source e.g. PEP National.

Matching Names


EXAMPLE

Name to be screened is Port of Marina. All PEP and OB sources options are turned ON for the group you are assigned to:

When screening the unspecified entity Port of Marina, the system will return all World-Check profiles matching the search, including all PEP and OB profiles:

Name to be screened is Port of Marina. All PEP and OB sources options are turned OFF for the group you are assigned to:

Matching Names


When screening Port Of Marina, then no PEP and OB World-Check profiles will be returned:

Note: The first match (Valor) returned when All Sources are turned ON for the Group, is also shown in the second sample where PEP and OB sources are turned OFF because the World-Check profile is also present for S (Sanctions)

How to Screen Names as Last Name – First Name


How to Screen Names as Last Name – First Name

Typically in some Asian countries such as China, the norm is to use the Last Name first and then the Given Name(s) for Individual entity type only. In World-Check One you can enter Last Name followed by ‘comma’ and then First Name when screening. With the example of Bo Xilai that if screened as Bo Xilai it returns a medium match to Xilai Bo, but if Screened as ‘Bo, XIlai’, then it returns an exact match.

EXAMPLE 1

Entering Last Name then First Name:

Matching results:

EXAMPLE 2

Entering Last Name and First Name separated by comma:

Matching results:

Frequently Asked Questions



Question Answer

What is the best order for Individuals names?

In order to get the best out of the matching engine it is recommended that names are provided in the order of Given name followed by Family name, example: John Smith.

If more than one given name is available they should be given in order, example: John Jacob Paul Smith

What happens to punctuation? Punctuation is stripped out of the search term. For example; a name that carries a hyphen “-“ the name will be matched with the hyphen, without it and without it with the hyphenated words concatenated. Statistical preference will be given to matches that are an exact match (i.e. with the hyphen present and in the right place if returned).

What happens to honorifics? Honorifics are removed from both sides of the matching process in order to create a level baseline for matching honorific’s such as Mr., Mrs., The Right Honorable, Lord, Lady etc are removed from both the search term and the search candidates to ensure the match strength is calculated on name match only. It is therefore safe to include honorifics in your search if you want to.

What is meant by the term “fuzzy matching”?

Fuzzy matching is a technology assisted method of identifying names across two data sets where there may be differences in the names but similarities are still required to be returned in order to capture similar names within the data to account for the unknown differences.

Fuzzy matching may account for a lot of variations

examples of which may be:

Missing name components e.g. John Smith to John

Jacob Robert Smith

Translation of names e.g. Jack to Jaques

Colloquial variations e.g. Thomas to Tom

Phonetic variations e.g. Dixon to Dickson

Minor spelling variations e.g. John Simth to John Smith

If I use fuzzy matching wont I get a lot of false positives?

Using a fuzzy matching setting will certainly produce more results than searching for an exact match but the matching technology used also accounts for probability to produce the most likely matches for names that have matched with under fuzzy comparison.

A name search will produce a number of results (candidates) and each of these will be evaluated alongside its peers (candidates) to establish the best possible candidate. Each other candidate is evaluated against this to establish the likelihood that a fuzzy match is a close match.



Question Answer

How should I decide whether to be fuzzy or more exact in my searches?

There are a number of different factors one needs to evaluate when choosing an optimum setting. One of those will be the quality of the data you are supplying for searching. If the data is good and there is confidence that all name elements have been captured then a tighter match setting should be more appropriate.

I get a lot of matches against alias of a single word i.e. “bird” how can I avoid this?

Increasing the matching score will remove some of these matches but a match still may occur when there is a strong match to the single word i.e. Richard Bird matching Bird. This can be avoided by using World-Check premium data that enables you to remove low quality aliases from your search.

Is the matching technology case sensitive? No

Appendix A – Name Variation Examples


Appendix A – Name Variation Examples

Variation Example

Phonetic and/or spelling differences Nayif Hawatmeh and Nayif Hawatma

Missing name components Mohammad Salah and Mohammad Abd El-Hamid Salah

Reordered name components Zedong Mao and Mao Zedong; Clara Schumann and schuman kelala

Rarity of a shared name component Two English names that contain Ditters are more likely to match than two names that contain Smith

Initials John F. Kennedy and John Fitzgerald Kennedy

Nicknames Bobby Holguin and Robert Holguin

"Cousin" or cognate names Pedro Calzon and Peter Calzon

Uppercase/Lowercase Rosa Elena PACHECO and Rosa Elena Pacheco

Initials John F. Kennedy and John Fitzgerald Kennedy

Uppercase/Lowercase Barack Obama and Barack OBAMA

Nicknames

"Cousin" or cognate names

Variable Segmentation Henry Van Dick and Henry VanDick

The similarity score of Carl Lepsius and Carol Lepsius reflects a mismatch between the likely genders of persons referred to by those names.

Corresponding name fields For [Katherine][Anne][Cox], the similarity with [Katherine][Ann] [Cox] is higher than the similarity with [Katherine Ann][Cox]

Appendix B – Stop Word Examples


Appendix B – Stop Word Examples

Organizations

Incorporated; corporation; partnership; management; holdings; shipping; company; limited; lines; marine; corp; sarl; ship; inc; llc; llp; srl; ltd; pty; plc; pte; pcl; sco; sna; sac; sms; the; and; of

Individuals

2nd lieutenant; acting president; administrative officer; admiral; ambassadors; assistant director of operations; assistant district attorney; assistant editor; assistant general manager; assistant manager; assistant secretary of state; ayatollahs; bank manager; baron; barrister; bishop; ceo; chairman; chairman lord; chairwoman; chancellor; chancellor of the exchequer; chief cabinet secretary;chief executive officer; chief finance minister; chief financial officer; chief international correspondent; chief judge; chief rabbi; commander; congressm[ae]n; dean; defence chief; defen[cs]e ministers; deputy chief officer; director; district prosecutor; doctors; dr; editor; editor in chief; education minister;emir; emir sheikh; ex-president; executive director; executive secretary; executive vice president; federal health minister; federal judges; finance minister; first lady; first officer; foreign-secretary; general; general counsel; general manager; governors; head judge; head of security; health minister; her royal highness; high commissioner; imam; immigration minister; intelligence officer; interim prime minister; judges; justice; justice commissioner; kings; lady; lance-corporal; lands minister; magistrates; maj; major-general; manager; managing director; minister of defense; miss; mrs; ms; mufti; mukhtar; mullah; national secretary; officer; oil minister; operations chief; petty officer 2nd class; police chief office; pope; prelate; premiers; queen's counsel; rabbis; ranger; regional director; secretary; secretary-general; senator; staff director; trade union leader; transport minister; union leader; union president; university president; veterinary doctor; vice-chairman; vice-governor; vice-minister of health; war correspondent; yogi

Appendix C – Languages


Appendix C – Languages

The matching technology (matching engine) used by World-Check One supports the following language and related scripts:

Language Scripts

Arabic Arabic

Chinese Han (Hanzi), Han (Simplified variant), Han (Traditional variant)

Dari

Arabic

English

Latin

French Latin

German Latin

Italian Latin

Japanese

Han (Kanji), Hiragana, (alias for Hiragana + Katakana), Japanese (alias for Han + Hiragana + Katakana), Katakana

Korean Hangul (Hangeul, Hankul), Han (Hanja)

Persian

Arabic

Portuguese Latin

Pushto

Arabic

Russian

Cyrillic

Spanish

Latin

Urdu

Arabic

Western Farsi Arabic

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Document Version: 0.2 Issue Date: 26 Nov 2016

thomson reuters world-check one...world-check one simplifies and accelerates the customer due...

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