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

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

2. Entity Analytic Solutions

3. 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

4. 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

5. 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

6. 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.

7. 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

8. 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

9. We’ll be back with Global Names…

10. 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

11. 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

12. 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

13. EAS Entity Resolution – Risk View Entity #144465 #144465 Marc R Smith Randal Smith Mark Randy Smith 123 Main St

14. 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

15. 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

16. 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

17. 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

18. EAS Relationship Resolution/Social Networks Entity #1230431

19. 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!

20. 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

22. 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?

23. 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

24. Danger Will Robinson!!!

25. Relationship Resolution

26. 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

27. $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

28. 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

29. 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 !

30. 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

31. 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

32. 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

33. Name Processing Magic!

34. 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

35. 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

36. 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

37. ? ? ? ? ? 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,

38. 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

39. MARIA ELENA LOPEZ GARCIA MARIA ELENA MARIA ELENA LOPEZ GARCI LOPEZGARCIA 39 What We Found The First Problem Database Problems

40. 40 What We Found The Second Problem Ineffective Search Technologies  Database Problems Exact Match Soundex (1918) NYSIIS (1963) “Home - Grown”

41. The Original Hollerith Machine for which Soundex was Designed

42. 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”

43. 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

44. 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

45. 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)

46. 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

47. 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

48. 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

49. 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

50. 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