Benford's Law: Theory, The General Law Of Relative Quantities, And Forensic Fraud Detection Applications
Contrary to common intuition that all digits should occur randomly with equal chances in real data, empirical examinations consistently show that not all digits are created equal, but rather that low digits such as {1, 2, 3} occur much more frequently than high digits such as {7, 8, 9} in almost all data types, such as those relating to geology, chemistry, astronomy, physics, and engineering, as well as in accounting, financial, econometrics, and demographics data sets. This intriguing digital phenomenon is known as Benford's Law.This book gives a comprehensive and in-depth account of all the theoretical aspects, results, causes and explanations of Benford's Law, with a strong emphasis on the connection to real-life data and the physical manifestation of the law. In addition to such a bird's eye view of the digital phenomenon, the conceptual distinctions between digits, numbers, and quantities are explored; leading to the key finding that the phenomenon is actually quantitative in nature; originating from the fact that in extreme generality, nature creates many small quantities but very few big quantities, corroborating the motto 'small is beautiful', and that therefore all this is applicable just as well to data written in the ancient Roman, Mayan, Egyptian, and other digit-less civilizations.Fraudsters are typically not aware of this digital pattern and tend to invent numbers with approximately equal digital frequencies. The digital analyst can easily check reported data for compliance with this digital law, enabling the detection of tax evasion, Ponzi schemes, and other financial scams. The forensic fraud detection section in this book is written in a very concise and reader-friendly style; gathering all known methods and standards in the accounting and auditing industry; summarizing and fusing them into a singular coherent whole; and can be understood without deep knowledge in statistical theory or advanced mathematics. In addition, a digital algorithm is presented, enabling the auditor to detect fraud even when the sophisticated cheater is aware of the law and invents numbers accordingly. The algorithm employs a subtle inner digital pattern within the Benford's pattern itself. This newly discovered pattern is deemed to be nearly universal, being even more prevalent than the Benford phenomenon, as it is found in all random data sets, Benford as well as non-Benford types.
1136611274
Benford's Law: Theory, The General Law Of Relative Quantities, And Forensic Fraud Detection Applications
Contrary to common intuition that all digits should occur randomly with equal chances in real data, empirical examinations consistently show that not all digits are created equal, but rather that low digits such as {1, 2, 3} occur much more frequently than high digits such as {7, 8, 9} in almost all data types, such as those relating to geology, chemistry, astronomy, physics, and engineering, as well as in accounting, financial, econometrics, and demographics data sets. This intriguing digital phenomenon is known as Benford's Law.This book gives a comprehensive and in-depth account of all the theoretical aspects, results, causes and explanations of Benford's Law, with a strong emphasis on the connection to real-life data and the physical manifestation of the law. In addition to such a bird's eye view of the digital phenomenon, the conceptual distinctions between digits, numbers, and quantities are explored; leading to the key finding that the phenomenon is actually quantitative in nature; originating from the fact that in extreme generality, nature creates many small quantities but very few big quantities, corroborating the motto 'small is beautiful', and that therefore all this is applicable just as well to data written in the ancient Roman, Mayan, Egyptian, and other digit-less civilizations.Fraudsters are typically not aware of this digital pattern and tend to invent numbers with approximately equal digital frequencies. The digital analyst can easily check reported data for compliance with this digital law, enabling the detection of tax evasion, Ponzi schemes, and other financial scams. The forensic fraud detection section in this book is written in a very concise and reader-friendly style; gathering all known methods and standards in the accounting and auditing industry; summarizing and fusing them into a singular coherent whole; and can be understood without deep knowledge in statistical theory or advanced mathematics. In addition, a digital algorithm is presented, enabling the auditor to detect fraud even when the sophisticated cheater is aware of the law and invents numbers accordingly. The algorithm employs a subtle inner digital pattern within the Benford's pattern itself. This newly discovered pattern is deemed to be nearly universal, being even more prevalent than the Benford phenomenon, as it is found in all random data sets, Benford as well as non-Benford types.
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Benford's Law: Theory, The General Law Of Relative Quantities, And Forensic Fraud Detection Applications

Benford's Law: Theory, The General Law Of Relative Quantities, And Forensic Fraud Detection Applications

by Alex Ely Kossovsky
Benford's Law: Theory, The General Law Of Relative Quantities, And Forensic Fraud Detection Applications

Benford's Law: Theory, The General Law Of Relative Quantities, And Forensic Fraud Detection Applications

by Alex Ely Kossovsky

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Overview

Contrary to common intuition that all digits should occur randomly with equal chances in real data, empirical examinations consistently show that not all digits are created equal, but rather that low digits such as {1, 2, 3} occur much more frequently than high digits such as {7, 8, 9} in almost all data types, such as those relating to geology, chemistry, astronomy, physics, and engineering, as well as in accounting, financial, econometrics, and demographics data sets. This intriguing digital phenomenon is known as Benford's Law.This book gives a comprehensive and in-depth account of all the theoretical aspects, results, causes and explanations of Benford's Law, with a strong emphasis on the connection to real-life data and the physical manifestation of the law. In addition to such a bird's eye view of the digital phenomenon, the conceptual distinctions between digits, numbers, and quantities are explored; leading to the key finding that the phenomenon is actually quantitative in nature; originating from the fact that in extreme generality, nature creates many small quantities but very few big quantities, corroborating the motto 'small is beautiful', and that therefore all this is applicable just as well to data written in the ancient Roman, Mayan, Egyptian, and other digit-less civilizations.Fraudsters are typically not aware of this digital pattern and tend to invent numbers with approximately equal digital frequencies. The digital analyst can easily check reported data for compliance with this digital law, enabling the detection of tax evasion, Ponzi schemes, and other financial scams. The forensic fraud detection section in this book is written in a very concise and reader-friendly style; gathering all known methods and standards in the accounting and auditing industry; summarizing and fusing them into a singular coherent whole; and can be understood without deep knowledge in statistical theory or advanced mathematics. In addition, a digital algorithm is presented, enabling the auditor to detect fraud even when the sophisticated cheater is aware of the law and invents numbers accordingly. The algorithm employs a subtle inner digital pattern within the Benford's pattern itself. This newly discovered pattern is deemed to be nearly universal, being even more prevalent than the Benford phenomenon, as it is found in all random data sets, Benford as well as non-Benford types.

Product Details

ISBN-13: 9789814651202
Publisher: World Scientific Publishing Company, Incorporated
Publication date: 10/13/2014
Pages: 672
Product dimensions: 5.90(w) x 8.90(h) x 1.10(d)

Table of Contents

Benford's Law v

Foreword vii

Introduction ix

Acknowledgment xiii

Section 1 Benford's Law 1

1 Digits versus Numbers 3

2 To Find Fraud, Simply Examine Its Digits! 5

3 First Leading Digits 8

4 Empirical Evidence from Real-Life Data on Digit Distribution 9

5 Physical Clues of the Digital Pattern 15

6 Historical Background of the Two Discoverers 18

7 Benford's Law 21

8 The Prevalence of Benford's Law 29

9 Physical Law versus Numerical Law 31

10 Nature's Way of Counting Single-Issue Phenomena 33

11 Case Study I: Time Between Earthquakes 38

12 Data on Population Counts of Cities, Towns, Regions, and Districts 41

13 Case Study IL U.S. Census Data on Population Centers 42

14 Data sets on USA Population by State and by County 46

15 Four Distinct Numerical Processes Leading to Benford 48

16 Random Linear Combinations and Accounting Revenue Data 49

17 Aggregation of Data Sets as a Prominent Cause of Benford's Law 53

18 Random Pick from a Variety of Data Sources is Logarithmic 55

19 Integral Powers of Ten 57

20 The Logarithmic as Repeated Multiplications 58

21 Case Study III: Exponential 0.5% Growth Series for 3,233 Periods 67

22 Case Study IV: 140 Cumulative Dice Multiplications 70

23 The Universality of Benford's Law -True in any Scale System 72

24 A Hidden Digital Signature within Benford's Digital Signature 74

Section 2 Forensic Digital Analysis & Fraud Detection 77

25 Historical Background of the First Applications of Benford's Law 79

26 Methods in Financial and Accounting Fraud Detection 81

27 The Part and Type of Data Applicable to Forensic Testing 88

28 Case Study V: .U.S. Market Capitahzation on January 1, 2013 96

29 Case Study VI: Microsoft Corporation Financial Statement 98

30 Case Study VII: Total Return of Athena Guaranteed Futures Fund 100

31 Establishing Direct Connection Between Digit Anamoly & Fraud 102

32 Post-Test Conclusions 106

33 Detecting Fraud via Digital Development Pattern 108

34 The Dilemma of FTD versus LTD for Digit-Anemic Numbers 110

Section 3 Data Compliance Tests 113

35 Testing Data for Conformity to Benford's Law 115

36 The Z Test 120

37 The chi-ScjuareTest 123

38 SSD as a Measure of Distance from the Logarithmic 128

39 Saville Regression Measure 134

40 Value Repetition Test 138

41 The Confusion and Mistaken Applications of Summation Test 141

42 Summation Test in the Context of Fraud Detection 147

43 Methods in Digital Development Pattern Detection 149

44 Case Study VIII: Price List of a Large Manufacturer 164

45 Case Study IX: USA County Area Data 172

46 Random Linear Combinations and Revenue Data Revisited 177

47 Case Study X: Forensic Analysis of Revenue Data for Small Shop 192

Section 4 Conceptual and Mathematical Foundations 195

48 Hybrid Data Sets Blending Several Data Types 197

49 Second-Generation Distributions 198

50 A Leading Digits Parable 200

51 Simple Averaging Scheme as a Model for Typical Data 207

52 More Complex Averaging Schemes 212

53 Digital Proportions within the Number System Itself 216

54 Chains of Distributions 219

55 Hill's Super Distribution 227

56 The Scale Invariance Principle 230

57 Philosophical and Conceptual Observations 233

58 Some General Results 237

59 Density Curves and Their Leading Digits Distributions 242

60 The Case of k/x Distribution 246

61 Uniform Mantissa, Varied Significand, and the General Law 253

62 Uniqueness of k/x Distribution 261

63 Related Log Conjecture 266

64 Testing Related Log Conjecture via Simulations 271

65 The Lognormal Conjecture of Hill's Super Distribution 275

66 Non-Symmetric Related Log Curves 279

67 Wide Range on the Log-Axis and Logarithmic Behavior 281

68 The Remarkable Malleability of Related Log Conjecture 282

69 Hill's Super Distribution and Related Log Conjecture 293

70 Scale Invariance Principle and Related Log Conjecture 295

71 The Near Indestructibility of Higher Order Distributions 297

72 Falling Density Curve with a Tail to the Right 301

73 Falling Density Curve with a Particular Steepness 305

74 Fall in Density is Well-Coordinated Between IPOT Values 307

75 Synthesis Between the Deterministic and the Random 313

76 Dichotomy Between the Deterministic and the Random 317

77 Fitting the Random into the Deterministic 327

78 The Random Flavor of Population Data 332

79 The Lognormal Distribution and Benford's Law 335

80 Scrutinizing Digits within Lognormal, Exponential, and k/x 339

81 Leading Digits Inflection Point 345

82 Digital Development Pattern Found in all Real-Life Random Data 349

83 Digital Development Pattern Seen Only Under [POT Partition 356

84 Development Pattern More Prevalent than Benford's Law Itself 360

85 Sum-Invariant Characterization of the Law (Summation Test) 363

Section 5 Benford's Law in the Physical Sciences 373

86 Mother Nature Builds and Destroys with Digits in Mind 375

87 Quantum Mechanics,Thermodynamics, and Benford's Law 377

88 Chemistry, Random Linear Combinations, and Benford's Law 380

89 Benford's Law and the Set of all Physical Constants 387

90 MCLT as an Explanation for Single-Issue Physical Phenomenon 389

91 Chains as an Explanation for Single-Issue Physical Phenomenon 395

92 Breaking a Rock Repeatedly into Small Pieces is Logarithmic 398

93 Random Throw of Balls into Boxes Approximating the Logarithmic 402

94 Logarithmic Model for Planet and Star Formations 415

95 Hybrid Causes Leading to Logarithmic Convergence 419

96 Mild Deviations Seen in Small Samples of Logarithmic Data Sets 421

97 The Remarkable Versatility of Benford's Law 423

Section 6 Topics in Benford's Law 425

98 Singularities in Exponential Growth Series 427

99 Super Exponential Growth Series 439

100 Higher-Order Leading Digits 442

101 Digit Distributions Assuming Other Bases 450

102 Chains of Distributions Revisited 452

103 Chainable Distributions and Parameters 462

104 Frank Benford's Averaging Scheme as a Distribution Chain 471

105 Effects of Parametrical Transformations on Leading Digits 474

106 Digits of the Wald, Weibull, chi-square, and Gamma Distributions 479

107 Digital Patterns of the Exponential Distribution 480

108 Saville Regression Measure Revisited 484

109 The Scale Invariance Principle and AGD Interpretation 497

110 Case Study XI: Large Sample from a Variety of Data Sources 501

111 Direct Expression of first Digit for any Number - Computer Use 506

112 Artificially Creating Nearly Perfect Logarithmic Data 507

Section 7 The Law of Relative Quantities 509

113 The Relating Concepts of Digits, Numbers, and Quantities 511

114 Benford's Law in its Purest Form 513

115 Number System Invariance Principle 5 19

116 Cartesian Coordinate System is Number-System-Invariant 522

117 Physics is Number-System-Invariant 524

118 Multiplicative CLT is Number-System-Invariant 527

119 Greek Parable and Chains are Number-System-Invariant 529

120 Physical Reality versus Digital Perception 530

121 Patterns in Physical Data Transcend Number Systems and Digits 531

122 Common Thread Going Through Multiple Physical Data Sets 532

123 Casting a Repetitive Bin System to Measure Fall in Histogram 535

124 Non-Expanding Bin System Measuring Fall in k/x Distribution 542

125 Once-Expanding Bin System Measuring Fall in k/x Distribution 547

126 Once-Expanding Bins for k/x Reduces to Benford when F = D + 1 549

127 Twice-Expanding Bin System Measuring Fall in k/x Distribution 550

128 Twice-Expanding Bins for k/x Reduces to Benford when F - D + 1 552

129 Infinitely Expanding Bin System Measuring Fail in k/x 554

130 Confirmation Matching k/x Fall with Empirical Bins on Real Data 556

131 Closed Form Expression for the Limit of the Infinite Sequence 558

132 Closed Form Expression for the Limit in the Flat Case F = 1 563

133 9-Bin Systems with F = 10 on Real Data AH Yield LOGTEN(1 + 1/d) 567

134 Bin Systems Need to Start Near Origin with Small Initial Width 569

135 Actual or Degree of Compliance May Be Bin- and Base-Variant 575

136 Correspondence in Data Classification Between Bin Systems and BL 582

137 F = D + 1 Bin Systems on Real Data Yield LOGBASE(1+1/d) 590

138 The Remarkable Malleabihty and Universality of Bin Schemes 591

139 Higher-Order Digits Interpreted as Particular Bin Schemes 602

140 Bin Development Pattern 608

141 The General Scale Invariance Principle 614

142 Paradoxes Explained 619

143 Digits Serving as Quantities in Benford's Law 622

144 Frank Benford's Prophetic Words 624

145 Future Direction 625

146 The Universal Law of Relative Quantities 626

147 Dialogue Concerning the Two Chief Statistical Systems 628

References 637

Glossary of Frequently Used Abbreviations 643

Index 645

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