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Catching the bad guys (and seeing the good guys) Entity/Relationship Analytics and how to Understand/Recognize Global Names. Entity Analytic Solutions. LEVEL 2) Cultural Obstacles. LEVEL 5) Privacy & Security. LEVEL 1) Dirty Disparate Data. LEVEL 3) Identity Ambiguation. LEVEL 4)

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slide1

Catching the bad guys (and seeing the good guys) Entity/Relationship Analytics and how to Understand/Recognize Global Names

entity analytic challenges ability to overcome multiple levels of identity ambiguation

LEVEL 2)

Cultural Obstacles

LEVEL 5)

Privacy & Security

LEVEL 1)

Dirty Disparate Data

LEVEL 3)

Identity Ambiguation

LEVEL 4)

Network Ambiguation

Entity Analytic Challenges:Ability to Overcome Multiple Levels of Identity Ambiguation

Naturally Occurring Phenomena Such As Data Quality And Cultural Variants, As Well As Deliberate Acts Of Identity Misrepresentation, Compounded by Need to Protect Privacy

what is needed to address the challenge step one address naturally occurring data quality issues
What is Needed To Address The Challenge?Step One – Address Naturally Occurring Data Quality Issues

The first step is to gather the information assets necessary to accomplish the mission, and perform consistent quality, standardization, formatting, and enhancement

Perform Data Quality

(Naturally Occurring)

Complexity Level HIGH

  • Transposition

Errors

  • Multiple Formats
  • Data Drift
  • Dirty Data
architecturally thinking
Architecturally Thinking
  • We can handle dirty data…
  • But WHAT is dirty data?
  • Do we always want to cleanse data?
  • What is the value of dirty data?
  • Consider the sources of data
  • Consider the flow of data
the problem ambiguous misrepresented blurry identity
The Problem: Ambiguous, Misrepresented, Blurry Identity

For a variety of reasons, companies don't have a clear picture of the individuals and organizations with whom they do business.

Depending on the nature of the organization's mission, the impact can lead to problems including missing threats to public safety, duplication of benefit payments, accepting business from known criminals, etc.

slide7

Who knows who?

Who does what?

Who is who?

  • Obvious & non-obvious
  • Links people & groups
  • Role alerts
  • Events & Transactions
  • Business Rule Monitoring
  • Criteria based alerting
  • Establish Unique Identity
  • Integrates data silos
  • Full attribution of entities

IBM InfoSphere Relationship Resolution

Relationship

Identity

Event

what is needed to address the challenge step two manage cultural identity ambiguation
What is Needed To Address The ChallengeStep Two – Manage Cultural Identity Ambiguation

The second step is managing the cultural variations of identity data such as name variants, spellings, cultures, genders and applying culture-specific analytics to the recognition process

Perform Data Quality

Cultural Ambiguities

(Naturally Occurring)

(Naturally Occurring)

Complexity Level HIGH

Complexity Level HIGH

  • Transposition

Errors

  • Multiple Formats
  • Data Drift
  • Dirty Data
  • Name Cultures
  • Name Genders
  • Name Order
what is needed to address the challenge step three address intentional identity
What is Needed To Address The Challenge?Step Three – Address Intentional Identity

Resolve “Who Is Who” – Resolve and identify persons/organizations deliberately trying to hide or misrepresent who they actually are

Resolve Identity

Perform Data Quality

Cultural Ambiguities

(Intentional Act)

(Naturally Occurring)

(Naturally Occurring)

Complexity Level VERY HIGH

Complexity Level HIGH

Complexity Level HIGH

  • Transposition

Errors

  • Multiple Formats
  • Data Drift
  • Dirty Data
  • Name Cultures
  • Name Genders
  • Name Order
  • Identity Masking
  • False Identifiers
  • Stolen Identifiers
entity analytic solutions who is who
Entity Analytic Solutions – “Who is who?”

!

Interactions

Observations

Entity Context

EAS Entity #9451

Attribute Value Source

Name Marc R Smith A-70001

Address 123 Main St A-70001

Phone (713) 730 5769 A-70001

Tax ID 537-27-6402 A-70001

License 0001133107 A-70001

Record #70001

Marc R Smith

123 Main St

(713) 730 5769

537-27-6402

DL: 0001133107

A

EAS Entity #9453

Attribute Value Source

Name Marc R Smith A-70001

Name Randal M Smith B-9103

Name Mark Randy Smith C-6251

Address 123 Main St A-70001

Address 456 First St C-6251

Phone (713) 730 5769 A-70001

Phone (713) 731 5577 B-9103

Phone (713) 731 5577 C-6251

Tax ID 537-27-6402 A-70001

License 0001133107 A-70001

License 1133107 C-6251

DOB 06/17/1934 B-9103

EAS Entity #9453

Attribute Value Source

Name Marc R Smith A-70001

Name Mark Randy Smith C-6251

Address 123 Main St A-70001

Address 456 First St C-6251

Phone (713) 730 5769 A-70001

Phone (713) 731 5577 C-6251

Tax ID 537-27-6402 A-70001

License 0001133107 A-70001

License 1133107 C-6251

EAS Entity #9452

Attribute Value Source

Name Randal M Smith B-9103

DOB 06/17/1934 B-9103

Phone (713) 731 5577 B-9103

B

Record #9103

Randal M Smith

DOB: 06/17/1934

(713) 731 5577

Identity

C

Record #6251

Mark Randy Smith

456 First Street

(713) 731 5577

DL:1133107

  • Sequence Neutral Identity Resolution
  • Self-Correcting
  • 20 Attributes Out of the Box
  • Predefined Rules/Sensitivities

A – Credit Card B – Mortgage C – DDA

Marc# 9453

eas entity resolution the basis for assessment

Mr. Joey Carbello555 Church AveNew York, NY 10070Tel#:212-693-5312DL#:544 210 836

PPN#: 086588345

ACCT #494202

OBLIGOR

Mr. Joseph Carbella55 Church StreetNew York, NY 10007Tel#: 212-693-5312DOB: 07/08/66SID#: 068588345DL#: 544 210 836

ACCT # 2310322

COSIGNER

Mr. Joe Carbello1 Bourne StClinton MA 01510TEL#: 978-365-6631DL#:544 210 836DOB: 07/09/66

ACCT #3292322

HOME LOAN

Mr. Joe JonesAPT 4909Bethesda, MD 20814Tel#:978-365-6631DOB:09/07/66

AUTO LOAN

Close match

Exact match

EAS Entity Resolution – The Basis for Assessment

Allows Investigators to Establish True Identity When Suspects, Attempt To Hide Or Blur Who They Are and Their Characteristics

eas entity resolution risk view
EAS Entity Resolution – Risk View

Entity #144465

#144465

Marc R Smith

Randal Smith

Mark Randy Smith

123 Main St

eas identity repository identity folder
EAS Identity Repository – Identity Folder

Bob Smith

#144465

Bob Smith

Mark Robert Smith

Marc R Smith

Mark Smith

Mark Robert Smith

Marc R Smith

Mark Smith

architecturally thinking15
Architecturally Thinking
  • How does this compare to federating data?
  • IS this MDM?
    • If yes, why?
    • If no, why?
  • Remember the Filing Cabinet
  • Remember the dirty data?
  • Consider your understanding of Single View
  • Remember that you now have a database footprint
what is needed to address the challenge step four address network ambiguation
What is Needed To Address The Challenge?Step Four – Address Network Ambiguation

Uncover “Who Knows Who” – Spot linkages or Non-Obvious Relationships between identities to reveal criminal networks, syndicates, and terrorist cells

Resolve Identity

Relate Identity

Perform Data Quality

Cultural Ambiguities

(Intentional Act)

(Intentional Act)

(Naturally Occurring)

(Naturally Occurring)

Complexity Level VERY HIGH

Complexity Level EXTREME

Complexity Level HIGH

Complexity Level HIGH

  • Nominees
  • Non Obvious

Relations

  • Hidden networks
  • Transposition

Errors

  • Multiple Formats
  • Data Drift
  • Dirty Data
  • Name Cultures
  • Name Genders
  • Name Order
  • Identity Masking
  • False Identifiers
  • Stolen Identifiers
entity analytic solutions who knows who

Marc is related to Joan from B by home address

Marc is related to Bob from B by a disclosed relationship.

Related to Alice (through Sue) from D by a phone number at two degrees of separation

Related to John (through Alice) from B by a business address at three degrees of separation

Related to Sue from C by a Tax ID at one degree of separation

Entity Analytic Solutions – “Who knows who?”

!

!

What relationships does Marc Smith hold with entities across the enterprise?

Joan# 1241

Alice# 9123

Relationship

A – Credit Card B – Mortgage C – DDA D – Wires E – Addl internal/ External

Bob# 6111

Sue# 5353

John# 2969

Marc# 9453

eas relationship resolution degrees of separation across any attribute s
EAS Relationship Resolution – Degrees of Separation( across any attribute(s) )

(Associative Property: If A = B = C; Therefore A = C)

=

=

C: Tom Sinclair

Addr: 123 Main St

*** OFAC LIST ***

A: Mark Smith

Phone: (713) 730 5769

B: Kate Green

Phone: (713) 730 5796

Addr: 123 Main St

=

Mark is related to Tom by Two Degrees of Separation.

C: Tom Sinclair

Addr: 123 Main St

*** OFAC LIST***

A: Mark Smith

Phone: (713) 730 5769

EAS Supports 30 Degrees of Separation!

slide20

144225

144465

144465

142365

143211

149965

144465

148965

144465

123101

144465

144465

143265

144465

144215

142145

Identity Folders – Complete Relationship Resolution

Kate Green

431 Rebus

Avenue

Harlow

Mark Smith

123 High St

Telford

Tom Sinclair

23 Lansbury Ave

Stratford

Raj Jones

65 Kenyan Way

Jim Roberts

30130 Elm

Boston, MA USA

Harold Burr

402 West St

Bristol

Juergen Lit

921 Rue de Lyon

Paris

Ming Chan

495 Randal St

Liverpool

Gwen Roberts

95 Arvale Road

London

Luci Tamoia

13 Galliard House

Leeds

architecturally thinking22
Architecturally Thinking
  • Degrees of separation…from?
    • Other people (identities)
    • Other “things” (entities)
  • EAS is a fraud detection system
    • If yes, why?
    • If no, why?
  • Remember the Filing Cabinet
  • Remember the dirty data?
  • The power of understanding a network of identities
  • EAS is rarely (never) “rip and replace”
    • If not, then where does it fit?
slide23

What is Needed To Address The Challenge? Accommodate Privacy & Security Considerations If Required

Anonymization – For situations where privacy or security concerns make recognition high risk, or sensitive data needs to be de-identified to facilitate cross-agency/country sharing and analytics

Resolve Identity

Relate Identity

Privacy & Security

Perform Data Quality

Cultural Ambiguities

(Intentional Act)

(Intentional Act)

(Naturally Occurring)

(Naturally Occurring)

(Reactive Action)

Complexity Level VERY HIGH

Complexity Level EXTREME

Complexity Level EXTREME

Complexity Level HIGH

Complexity Level HIGH

  • Nominees
  • Non Obvious

Relations

  • Hidden networks
  • Privacy

Compliance

  • Security Drivers
  • Transposition

Errors

  • Multiple Formats
  • Data Drift
  • Dirty Data
  • Name Cultures
  • Name Genders
  • Name Order
  • Identity Masking
  • False Identifiers
  • Stolen Identifiers
entity analytic solutions who does what
Entity Analytic Solutions – “Who does what?”

!

!

Observed Activities

Identity-based Aggregate

Business Rules & Threshholds

TRANSACTIONS

Acct #120-555

Withdraw $9,900

Acct #456-983

Withdraw $9,800

Acct #942-525

Withdraw $9,800

  • Sample Rules
  • Transaction Amt > $
  • Average Transaction Amt
  • Number of Transactions > X
  • Between Date A and Date B
  • Within Geospatial Range
  • Combinations of the Above
  • User Defined

Cust #C-6251

Mark Randy Smith

Acct #120-555

Cust #A-70001

Marc R Smith

Acct #456-983

Cust #B-9103

Randal M Smith

Acct #942-525

$29,500

Event

EVENTS

01/25/08 10:39

Account Applicant

01/25/08 10:55

Account Applicant

01/25/08 11:05

Account Applicant

3 Apps

  • Streaming Real-Time Monitoring & Alerting
  • (User) Define New Rules via GUI

Marc# 9453

slide27

$8,000 Cash Deposit

$9,900 Wire Transfer

$8,000 Cash Deposit

$8,000 Wire Transfer

$9,500 Cash Deposit

ACCT# 987-442-004

ACCT# 990-432-000

ACCT# 321-462-567

ACCT# 675-466-099

ACCT# 987-442-004

$8,000 Wire Transfer

ACCT# 321-462-567

$9,500 Cash Deposit

ACCT# 675-466-099

Traditional Anti Money Laundering – Account OrientationFraudsters know how to defeat account based detection systems

Entity Analytic Solutions – Identity OrientationEAS (identity based) Catches The Fraudsters at THEIR Game!

Account-Number-Based Analysis SolutionsPossess Blind-Spots

To defeat SAR systems criminals will “structure” activity across multiple accounts, each attached to an identity packet, and across multiple geographies so the suspicious pattern is watered down and overlooked.

$35,000

ALERT!

“Structuring remains one of the most commonly reported suspected crimes on Suspicious Activity Reports (SARs).” – BSA AML Examination Manual

architecturally thinking28
Architecturally Thinking
  • Real time “action resolution” through business rules
  • What are the business rules?
    • And who knows them?
  • Always, always, always consider data overload
  • A new term: False positive/False negative
  • They can be:
    • A great ROI tool when you reduce them
    • A REAL PROBLEM when you increase them
  • Think: Feedback loops
  • Think: Synergy
entity analytic solutions how do i find what i should know
Entity Analytic Solutions – “How do I find what I should know”

The “Enterprise Amnesia” Model

“Have we seen this applicant before?”

What is the right question to ask?

DATA

MART

New data? Must ask again every day

Are all the details still present

DW

DATA

MART

DATA

MART

Will I remember how these facts relate?

Merge/Purge introduces data loss when picking the “best version”

Data segragated to “support” dept initiatives

Days, Weeks or Months

Latency and Segregation are the “Bad Guys” Open Door

Days or Weeks

!

entity analytic solutions how do i find what i should know30

Entity Analytic Solutions

Entity Analytic Solutions – “How do I find what I should know”

!

The “Enterprise Awareness” Model

“Have we seen this applicant before?”

DATA

MART

Queries & Data Flow Through The Same Channel

DW

Process New Key Info First Like a Query

=

DATA

MART

Alerts pushed to analyst upon suspicious activity

DATA

MART

Each New Key Data Value Introduced is Evaluated Against All Prior Key Data Values

“The address and phone for account 59412 has changed 5 times in just 3 weeks. Alert! potential Identity Theft.”

” A bank employee changed their payroll address to the address of an ex-employee jailed for embezzlement three years ago.”

“This person has applied 10 times before and shares an address and SSN with a bank employee/teller in the same city.”

Catch The Bad Guys!Nominal Latency & Real-Time Contextto PRE-EMPTand PREVENT

Seconds – Streaming Real-Time

slide31

Entity Analytic Solutions Architecture

Service

Client

Process Search * Load * Score

CoreClient * Alert * Entity

VisualizerSearch

Graph

Research

Application Server

User

Database

Engine

ConsoleConfigure

Secure

Manage

Resolve

Relate

Entity Repository

Recognize

Full Attribution

Fully Auditable

Persistant Search & Alerts

Admin

eas architecture in the enterprise

External

Data Service(s)

Additional Data

On An Identity

Review & Act (manually/auto) on Conflicts in Identities and Relationships per Business Rules

External Lists

Of Bad Guys

Internal

Watch Lists

REAL-TIME

(or batched)

IDENTITY

RESOLUTION

EAS

Always Up-to-date

(No Reload)

Best Customers

Identity and

Relationship

Repository

M&A Data

Online Customers

Population in the

context of the

resolved identities

Employees

Vendors

EAS Architecture in the Enterprise

External Audience

Fraud

Detection

Data

Warehouse

Extranet Portal

Analysis /

Mining

Ability to aggregate by consolidated identities and their full attributes

Relationships available as another dimension

Intranet Portal

Extraction, Transformation,

Validation; Federated Data Access

CRM

Query/Reporting

Apps, e.g.

Visualization

Financial

Internal Audience

Enterprise

Transformation

Access

Data Source

Data Mart

ETL

Data Store

& Calculation

Metadata

Warehouse Administration

slide34

OFAC List

Check Was

Missed!!

There’s how many variants of that name?

How do you parse “Maria Luz Rodriguez v. de Luna”

Name Matching & Name Management Challenges Significant Business Issues

  • Basic Question Number 1: How do you handle the management of your name data?
  • Difficult to accurately search for & match customers family or cultural variants of first and last names
  • Can validate addresses & telephone numbers, but how do we know if a name is accurate?
  • We have invested millions in cleaning up our customer data file, yet problems remain
    • Current solutions based on very old technology and generate too many false positives & negatives, too time-consuming.
    • This is a pervasive problem in many industries
who cares about names
Who cares about names?

Large

Criticality of Name Data Management

Size of name data set

Small

Low

High

Risk posed by false negatives /

Requirement to handle names precisely

what s in a name
What’s In a Name?

36

  • Names remain the single most important means for identifying persona non grata
  • Biometrics are only useful the second time you meet someone
  • People everywhere in the world are learning how easily our name search systems can be confounded and circumvented
slide37

?

?

?

?

?

Why Are Multi-Cultural Names So Hard?How Do You Verify A Name?

Typically CIFs are focused on storing customer information, demographics, account information etc. They aren’t equipped to deal with the unique demands of classifying, matching and processing global & cultural name variations.

Name Order, Hussein, Mohammed Abu Ali

Titles, Dr., Rev, Haj, Sri., Col

Phonetics,Worchester, Wooster, “Worcester”

Nicknames, Drew, Manny, Cat

Shortened names, Andy, Eman

Prefixes,Abdul, Fitz, O', De La,

Mohaammad,

Mohammed,

Imhemmed,

Mohammd,

Mohamod,

Mohamud,

Andras,

André,

Andre,

Drue,

Ohndrae,

Ohndre

Eman,

Emanual,

Imanuel,

Immanuele,

Manny,

Manual,

Cait,

Caitey,

Katalin,

Katchen,

Kate,

Katerinka,

what s needed to address the challenge
What’s Needed to Address The Challenge?

38

Algorithm

The sole purpose of a

Search Engine

is to mediate between a

User and a Data Base

Successful

Name

Searching

User

Database

slide39

MARIA

ELENA

LOPEZ

GARCIA

MARIA ELENA

MARIA

ELENA

LOPEZ GARCI

LOPEZGARCIA

39

What We Found

The First Problem

Database Problems

slide40

40

What We Found

The Second Problem

Ineffective Search Technologies

 Database Problems

Exact Match

Soundex (1918)

NYSIIS (1963)

“Home - Grown”

slide42

42

What We Found

The Third Problem

Database

Limited User Support

Korean

Hispanic

Yoruban

Chinese

Search

Exact Match

Russian

Arabic

Soundex (1918)

Indonesian

Thai

NYSIIS (1963)

“Home-Grown”

simple name recognition is particularly hard

Zhang Qiusu

Chang Ch’iu-Su

Chiusu Sae Chang

Cheung Yau So

Cheung Yau So

Simple Name Recognition is Particularly Hard
  • There are hundreds of name variants
  • There are multiple ways that these names can be spelled -
  • You can verify an address, a telephone number, but how do you verify a name??

China

Taiwan

Myanmar

Hong Kong

Laos

(Burma)

Macau

Philippines

Thailand

Cambodia

Vietnam

Malaysia

Singapore

Indonesia

solution
Solution
  • Global Name Recognition consists of a set of tools that complement existing IT investments for organizations looking to analyze, search and process names
  • Domain expertise in multi-cultural names in the areas of:
    • Name Analysis
    • Name Enrichment
    • Name Matching
  • Adds value to name matching and analysis based on statistical and linguistic analysis of almost a billion names and 18 cultural families
  • Reduces false positive results so that the information returned is reliable and relevant

ERP Systems(HR, Contracts, etc.)

Customer Information Systems

Name-centric data warehouses

Anti-Money Laundering Systems

Global Name Recognition

Watch lists(OFAC, PEP, Interpol)

Name data files

3rd Party data sets

Search Lists

what is gnr
What is GNR ?
  • A series of Services Oriented Architecture enabled libraries and interfaces that address the linguistic and cultural complexities of names (personal, organizational,…) from around the world
  • Used to enhance name-processing (analysis, matching, understanding) in a wide variety of systems and applications
  • Based on 20+ years intensive research and data-collection of names – based on approximately 1 Billion name repository)
slide46

The Knowledge Base Process

GNR has implemented a knowledge based approach for coping with the wide array of multi-cultural name forms found in databases.

GNR Knowledge Base

  • Over 20 years in development
  • Information based, not rule based.
  • Over 200 countries studied and growing
  • Close to a billion names & linguistics, and growing
  • We have it, no one else does
  • Names are first submitted to an automatic analysis process, which determines the most likely cultural/linguistic origin of the name.
  • Based on this determination, an appropriate algorithm or set of rules is applied to the matching process.

Andreas, Andrei, Andrej, Andres, Andresj, Andrewes, Andrews, Andrey, Andrezj, Andrian, Andriel, Andries, Andrij, Andrija, Andrius, Andro, Andros, Andru, Andruw, Andrzej, Andy, Antero, Dandie, Dandy, Drew, Dru, Drud, Drue, Drugi, Mandrew, Ohndrae, Ohndre, Ondre, Ondrei, Ondrej, Ohnrey Ondrey, Eric, Erich, Erick, Erico, Erik, Eryk, Federico, Federigo, Fred, Fredd, Freddie, Freddy, Fredek, Frederic, Frederich, Frederico, Frederik, Fredi, Fredric, Fredrick, Fredrik, Frido, Friedel, Friedrich, Friedrick, Fridrich, Fridrick, Fritz, Fritzchen, Fritzi, Fritzl, Fryderky, Ric, Fredro, Rich, Rick, Ricky, Rik. Rikki. Cait, Caitie, Cate, Catee, Catey, Catie, Kaethe, Kait, Kaite, Kaitlin, Katee, Katey, Kathe, Kati, Katie, Bel, Belia, Belicia, Belita, Bell, Bella, Belle, Bellita, Ib, Ibbie, Isa, Isabeau, Isabela, Isabele, Isabelita, Isabell, Isabella, Isabelle, Ishbel, Isobel, Isobell, Isobella, Isobelle, Issie, Issy, Izabel, Izabella, Izabelle, Izzie, Izzy, Sabella, Sabelle, Ysabeau, Ysabel, Ysabella, Ysobel, Ainslaeigh, Ashalee, Ashalei, Ashelei, Asheleigh, Asheley, Ashely, Ashla, Ashlan, Ashlay, Ashle, Ashlea, Ashleah, Ashlee, Ashlei, Ashleigh, Ashlen, Ashli, Ashlie, Ashly, Boutros, Par, Peder, Pedro, Pekka, Per, Petar, Pete, Peterson, Petr, Petre, Petros, Petrov, Pierce, Piero, Pierre, Piet, Pieter, Pietro, Piotr, Pyotr, Hamid, Hammad, Mahmood, Mahmoud, Mahmud, Mahomet, Mehmet, Mehmood, Mehmoud, Mehmud, Mihammad, Mohamad, Mohamed, Mohamet, Mohammad, Mohammed, Muhamet, Muhammed. Achmad, Achmed, Ahmaad, Ahmad, Ahmet, Ahmod, Amad, Amadi, Amahd, Amed. Amad, Amed, Amahd, Amadi, Ahmad, Amado, Amid, Umed. Iman, Imre, Imani, Imri, Imray, Ismat, Itai, Mead. Ad, Adamo, Adams, Adan, Adao, Addam, Addams, Addem, Addie, Addis, Addison, Addy, Ade, Adem, Adham, Adhamh, Adim, Adnet, Adnon, Adnot, Adom, Atim, Atkins Edom, Adem, Aindrea, Aindreas, Analu,

Classification:

Ohndre 89% German

Van Der Merve 90% Dutch

50 Variants

On-Dray

Search

Parsing

Parsing: “Van Der Merve, Ohndre”

Gender: Ohndre – 90% Male

Variants: 65 Variations

Phonetics: “On-Dray - Ohndre”

Noise: Ohndre, Ondre, Omdre,

Nicknames: Andy, Drew, Drus

Salutations: Mr, Mrs, Doctor, Haj,

German

Male 90%

Ohndre

  • Maria del Carmen Bustamante de la Fuente
  • Hisham Abu Ali Quereshi Noor Eldin
  • Chang Wen Ying
  • Nadezhda Ivanovna Ovtsyuk
  • William Martin Smith-Bagby Jr.
  • Ohndre Van Der Merve
slide47

IBM Global Name Management(Complete portfolio package – Minus the Encyclopedia)

IBM Global Name Analytics

IBM Global Name Scoring

Fully automated, high-performance multi-cultural name recognition and analysis

Global Name Reference Encyclopedia

Global Name Recognition

  • Global Name Management – is made up of 2 products:
    • Global Name Analytics
    • Global Name Scoring
  • Global Name Encyclopedia
  • Transliteration
    • Cyrillic
    • Latin-2
    • Greek
    • Arabic
global name analytics

IBM Global Name Analytics

Global Name Analytics
  • Identifies and classifies cultural background of a name
  • Determines country of association for a given name
  • Recognizes whether a name is predominantly male or female and provides relevant frequency statistics
  • Returns name variants and scores in order of their frequency of occurrence
  • Determines which name, of a given name combination, is likely to be the given name or surname
global name scoring aka name matching
Global Name Scoring (aka Name Matching)
  • Key capability: perform name matching against lists or other data sources
  • Improved accuracy of name searching, transliteration, and the quality of identity verification initiatives
  • Tuning capability for more than 40 parameters, allowing for highly tuned and application-specific results
  • Provides ranked search results based on similarity of pronunciation
  • Capability to accept names in native script for Arabic, Cyrillic and Greek languages and return results
  • KEY POINTS: Fast, accurate, scalable name matching

IBM Global Name Scoring

Input:Li-Hsiang Tsai

Search Results:

Tsai, Li Hsiang 1.0 116313

Tsai, Lishiang 0.99 102059

Cai, Li-Xiang 0.98 131620

Tasi, Li Hsiang 0.83 158987

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Global Name Reference Encyclopedia

Global Name Reference Encyclopedia

  • Standalone user-based web application
  • Lightweight, no need for integration with systems
  • Comprehensive, interactive reference tool for understanding names, their origins and history
  • Includes culture-specific information about names, their use, their meanings, and their patterns of spelling variations