Linguistic Identity Matching

257 Pages·2013·3.061 MB·English

by  Bertrand Lisbach, Victoria Meyer (auth.)

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Bertrand Lisbach
Victoria Meyer
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Linguistic
Identity
Matching
Linguistic Identity Matching
⋅
Bertrand Lisbach Victoria Meyer
Linguistic Identity Matching
BertrandLisbach VictoriaMeyer
Freiburg,Germany Zurich,Switzerland
ISBN978-3-8348-1370-1 ISBN978-3-8348-2095-2(eBook)
DOI10.1007/978-3-8348-2095-2
LibraryofCongressControlNumber:2013939631
SpringerVieweg
©SpringerFachmedienWiesbaden2013
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v
Foreword
by David Smith

My interest in identity matching comes from my years’ fraud investigation and
AML work. A few years ago, I would have expected that banks and other financial
institutions would, by now, have reduced the AML process to a routine, low level
back office function. How wrong I was!
At time of writing, the revolutions and uprisings in the Middle East have resulted
in wide-scale sanctions where identity matching will be critical. The death of
Osama bin Laden and the reported capture of a vast hoard of data will no doubt
start a major anti terrorist financing effort.
Looking at this from a bank’s point of view, this is a worrying time, given the scale
of fines and sanctions imposed by regulators on both the firms and individuals.
No decent bank wants to get caught out but this may not be as easy as it may
appear. Before looking at how Linguistics should be a major step forward in
Identity Matching it is worth considering the context in which it can be used in
AML work in financial firms.
The Wolfsberg Group 2009 Statement on AML Screening, Monitoring and Searching
usefully sets out 4 key areas involving matching:
• Screening (real time payment screening to prevent
sanctions and other breaches)
• Monitoring (either live or delayed monitoring for unusual
transactions)
• Client Screening (KYC not only at the opening of accounts
but also at appropriate periods thereafter)
• Searches (typically in response to regulatory enquiries,
updated sanctions lists, etc.)
The first major issue is scale – the volume of data in any large bank is vast, so the
only realistic way of searching for names is using technology. Large banks that
have grown, particularly by mergers, are frequently relying on legacy IT systems
that were never designed with AML matching in mind. In addition, customer data
may not be in the same format in different parts of the same bank and AML watch
lists are not yet standardised between the various issuing authorities.
The scale and complexity all conspire to make automated matching very difficult.
Because of the dangers of missing a relevant match (a “false negative”), often
vi Foreword
the systems are allowed to return large numbers of false positive hits, which then
need to be manually sorted.
It is also worth considering the information contained on AML / sanctions lists.
A full name with nationality, date of birth, address, etc would be very useful but
is obviously not readily available for drug lords or terrorists, so matching even
just a few names that are not presented in a set format presents a real challenge.
The information set out in this book is fascinating – the range of linguistic variations
is quite amazing. How any matching worked before must be something of a
mystery unless it is of the so-called exact name matching variety of a decade ago.
I am sure the book will be read with interest by AML professionals but I would
be disappointed if it were not studied very carefully by regulators and authorities
issuing AML / sanctions lists. If they are short of time I would recommend at least
chapter one, some of chapters three to six, which get to grips with linguistics, and
of course, chapter seven on errors. The how to match considerations are essential
for AML professionals but would also be a good place for regulators to start before
doing reviews of banks’ AML procedures.
To me, a linguistic approach to matching is a more logical starting point anyway.
Take, for example, a Mexican regional bank being ordered to search for names
of one of the murderous drug gangs. If the search was being done manually, the
staff, being local, would automatically be using their knowledge of the language,
common variations, etc. whereas the same search at, say, a Jersey bank would
not benefit from that local knowledge.
If linguistic matching can bridge that gap in knowledge then it could be key to
ensuring that the danger of false negatives is eliminated whilst helping to reduce
false positives.
David Smith is a forensic accountant with over 20 years’ experience leading
major fraud investigations. As the senior partner in KPMG Forensic Accounting, he
worked extensively throughout Europe and the Middle-East. He currently works
as a consultant dealing with investigations and corporate governance issues.
vii
Contents
Introduction: Paradigm Shift in Identity Matching Theory
Part I: Introduction to Linguistic Identity Matching 1
Chapter 1: Basic Concepts 5
1.1 Identity Matching and Name Matching 5
1.2 Database Profiles and Search Profiles 6
1.3 True and False Positives, True and False Negatives 8
1.4 Hit Rate and Search Accuracy (Recall and Precision) 9
1.5 Linguistic Identity Matching 10
Chapter 2: The Application of Identity Matching Techniques 12
2.1 Customer Relationship Management (CRM) 12
2.2 Anti-Money Laundering and Counter Terrorist Financing 16
2.3 Criminal Investigation and Crime Management 23
2.4 Information Search 25
2.5 Conclusion 26
Chapter 3: Introduction to Proper Names 28
3.1 Important Characteristics of Proper Names 28
3.2 The Historical Development of Naming Conventions 30
3.3 Western Names: the Germanic Example 31
3.4 Other Features of Western Naming Systems 33
3.5 The Russian Example 36
3.6 The Arabic Example 37
3.7 The Chinese Example 40
3.8 Conclusion 42
Chapter 4: Transcription 45
4.1 Transcription, Transliteration and Translation 46
4.2 Latin and Non-Latin Scripts 49
4.3 Romanisation of Cyrillic Names 52
4.4 Romanisation of Arabic Names 55
4.5 Romanisation of Names from the Chinese Script 60
4.6 Conclusion: Transcription as the Achilles Heel of Name Matching 65
viii Contents
Chapter 5: Derivative Forms of Names 68
5.1 Aliases and Derivative Names 68
5.2 Hypocorisms 70
5.3 Translated Names 73
5.4 Derivative and Translated Forms of Names of Legal Persons 76
Chapter 6: Phonetically Similar Names 77
6.1 Homophones 77
6.2 Linguistic Matching and Phonetics 80
Chapter 7: Typos 82
7.1 Variations, Spelling Mistakes and Typos 82
7.2 Motor Function and the Role of the Keyboard 83
7.3 Optical Character Recognition 85
7.4 Typos in the Identity Matching Process 86
Part II: Name Matching Methods 89
Chapter 8: Name Matching Methods of the First Generation 92
8.1 Introduction 92
8.2 Pattern Matching with Levenshtein Distance 93
8.3 Pattern Matching with N-Gram Methods 97
8.4 Phonetic Encoding with Soundex 99
8.5 Thesaurus-Based Matching Methods 102
8.6 Summary of the Application of First Generation Name Matching Methods 104
8.7 Reasons for the Continued Application of First Generation Methods 107
Chapter 9: Second Generation Name Matching Methods 113
9.1 Introduction 113
9.2 G2 Pattern Matching: Advancements of Edit Distance and N-Gram Methods 113
9.3 G2 Phonetic Encoding: Advancements on Soundex 117
9.4 Generative Algorithms for Name Variants 120
9.5 Summary of the Application of Second Generation Matching Methods 126
9.6 Conclusion: Three Decades of Name Matching 129
Chapter 10: Third Generation Name Matching Methods 130
10.1 Introduction 130
10.2 Principle Requirements for G3 Solutions 131
Contents ix
10.3 Linguistic Similarity Keys for Transcription and Homophones 137
10.4 Thesauri for Derivative Names and Special Cases 142
10.5 Generative Algorithms for Covering Simple Typos 143
10.6 Integration of Methods 147
10.7 Conclusion 151
Chapter 11: Benchmark Study 154
11.1 Introduction 154
11.2 Match Methods to be Assessed 154
11.3 Methodology and Findings 156
11.4 Conclusion 165
Part III: Into the New Paradigm 169
Chapter 12: Name Matching and Identity Matching 172
12.1 Space-Related Identity Attributes 173
12.2 Time-Related Identity Attributes 179
12.3 Classifying Attributes 182
12.4 Identification Codes 185
12.5 Integration of Single Attribute Comparisons 186
12.6 Conclusion 191
Chapter 13: Evaluation of Identity Matching Software 193
13.1 Introduction 193
13.2 Defining Requirements 195
13.3 Selecting Potential Candidates 199
13.4 Test Focus and Test Design 201
13.5 Analysis of Results 211
13.6 Conclusion 214
Chapter 14: A Linguistic Search Standard 216
14.1 The Need for a Search Standard 216
14.2 A Proposed Linguistic Search Standard 219
14.3 Defining a Corporate Linguistic Search Standard 220
14.4 Calculating the Match Level of a Full Name 229
14.5 Applying a Linguistic Search Standard 230
Index 233
Introduction:
Paradigm Shift in Identity
Matching Theory