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

  • Formāts: 672 pages
  • Izdošanas datums: 21-Aug-2014
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • Valoda: eng
  • ISBN-13: 9789814583701
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  • Formāts: 672 pages
  • Izdošanas datums: 21-Aug-2014
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • Valoda: eng
  • ISBN-13: 9789814583701
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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.
Benford's Law v
Foreword vii
Introduction ix
Acknowledgment xiii
Section 1: Benford's Law 1(76)
1 Digits versus Numbers
3(2)
2 To Find Fraud, Simply Examine Its Digits!
5(3)
3 First Leading Digits
8(1)
4 Empirical Evidence from Real-Life Data on Digit Distribution
9(6)
5 Physical Clues of the Digital Pattern
15(3)
6 Historical Background of the Two Discoverers
18(3)
7 Benford's Law
21(8)
8 The Prevalence of Benford's Law
29(2)
9 Physical Law versus Numerical Law
31(2)
10 Nature's Way of Counting Single-Issue Phenomena
33(5)
11 Case Study I: Time Between Earthquakes
38(3)
12 Data on Population Counts of Cities, Towns, Regions, and Districts
41(1)
13 Case Study II: U.S. Census Data on Population Centers
42(4)
14 Data sets on USA Population by State and by County
46(2)
15 Four Distinct Numerical Processes Leading to Benford
48(1)
16 Random Linear Combinations and Accounting Revenue Data
49(4)
17 Aggregation of Data Sets as a Prominent Cause of Benford's Law
53(2)
18 Random Pick from a Variety of Data Sources is Logarithmic
55(2)
19 Integral Powers of Ten
57(1)
20 The Logarithmic as Repeated Multiplications
58(9)
21 Case Study III: Exponential 0.5% Growth Series for 3,233 Periods
67(3)
22 Case Study IV: 140 Cumulative Dice Multiplications
70(2)
23 The Universality of Benford's Law -True in any Scale System
72(2)
24 A Hidden Digital Signature within Benford's Digital Signature
74(3)
Section 2: Forensic Digital Analysis & Fraud Detection 77(36)
25 Historical Background of the First Applications of Benford's Law
79(2)
26 Methods in Financial and Accounting Fraud Detection
81(7)
27 The Part and Type of Data Applicable to Forensic Testing
88(8)
28 Case Study V: U.S. Market Capitalization on January 1, 2013
96(2)
29 Case Study VI: Microsoft Corporation Financial Statement
98(2)
30 Case Study VII: Total Return of Athena Guaranteed Futures Fund
100(2)
31 Establishing Direct Connection Between Digit Anamoly & Fraud
102(4)
32 Post-Test Conclusions
106(2)
33 Detecting Fraud via Digital Development Pattern
108(2)
34 The Dilemma of FTD versus LTD for Digit-Anemic Numbers
110(3)
Section 3: Data Compliance Tests 113(82)
35 Testing Data for Conformity to Benford's Law
115(5)
36 The Z Test
120(3)
37 The chi-Square Test
123(5)
38 SSD as a Measure of Distance from the Logarithmic
128(6)
39 Saville Regression Measure
134(4)
40 Value Repetition Test
138(3)
41 The Confusion and Mistaken Applications of Summation Test
141(6)
42 Summation Test in the Context of Fraud Detection
147(2)
43 Methods in Digital Development Pattern Detection
149(15)
44 Case Study VIII: Price List of a Large Manufacturer
164(8)
45 Case Study IX: USA County Area Data
172(5)
46 Random Linear Combinations and Revenue Data Revisited
177(15)
47 Case Study X: Forensic Analysis of Revenue Data for Small Shop
192(3)
Section 4: Conceptual and Mathematical Foundations 195(178)
48 Hybrid Data Sets Blending Several Data Types
197(1)
49 Second-Generation Distributions
198(2)
50 A Leading Digits Parable
200(7)
51 Simple Averaging Scheme as a Model for Typical Data
207(5)
52 More Complex Averaging Schemes
212(4)
53 Digital Proportions within the Number System Itself
216(3)
54 Chains of Distributions
219(8)
55 Hill's Super Distribution
227(3)
56 The Scale Invariance Principle
230(3)
57 Philosophical and Conceptual Observations
233(4)
58 Some General Results
237(5)
59 Density Curves and Their Leading Digits Distributions
242(4)
60 The Case of k/x Distribution
246(7)
61 Uniform Mantissa, Varied Significand, and the General Law
253(8)
62 Uniqueness of k/x Distribution
261(5)
63 Related Log Conjecture
266(5)
64 Testing Related Log Conjecture via Simulations
271(4)
65 The Lognormal Conjecture of Hill's Super Distribution
275(4)
66 Non-Symmetric Related Log Curves
279(2)
67 Wide Range on the Log-Axis and Logarithmic Behavior
281(1)
68 The Remarkable Malleability of Related Log Conjecture
282(11)
69 Hill's Super Distribution and Related Log Conjecture
293(2)
70 Scale Invariance Principle and Related Log Conjecture
295(2)
71 The Near Indestructibility of Higher Order Distributions
297(4)
72 Falling Density Curve with a Tail to the Right
301(4)
73 Falling Density Curve with a Particular Steepness
305(2)
74 Fall in Density is Well-Coordinated Between IPOT Values
307(6)
75 Synthesis Between the Deterministic and the Random
313(4)
76 Dichotomy Between the Deterministic and the Random
317(10)
77 Fitting the Random into the Deterministic
327(5)
78 The Random Flavor of Population Data
332(3)
79 The Lognormal Distribution and Benford's Law
335(4)
80 Scrutinizing Digits within Lognormal, Exponential, and k/x
339(6)
81 Leading Digits Inflection Point
345(4)
82 Digital Development Pattern Found in all Real-Life Random Data
349(7)
83 Digital Development Pattern Seen Only Under IPOT Partition
356(4)
84 Development Pattern More Prevalent than Benford's Law Itself
360(3)
85 Sum-Invariant Characterization of the Law (Summation Test)
363(10)
Section 5: Benford's Law in the Physical Sciences 373(52)
86 Mother Nature Builds and Destroys with Digits in Mind
375(2)
87 Quantum Mechanics, Thermodynamics, and Benford's Law
377(3)
88 Chemistry, Random Linear Combinations, and Benford's Law
380(7)
89 Benford's Law and the Set of all Physical Constants
387(2)
90 MCLT as an Explanation for Single-Issue Physical Phenomenon
389(6)
91 Chains as an Explanation for Single-Issue Physical Phenomenon
395(3)
92 Breaking a Rock Repeatedly into Small Pieces is Logarithmic
398(4)
93 Random Throw of Balls into Boxes Approximating the Logarithmic
402(13)
94 Logarithmic Model for Planet and Star Formations
415(4)
95 Hybrid Causes Leading to Logarithmic Convergence
419(2)
96 Mild Deviations Seen in Small Samples of Logarithmic Data Sets
421(2)
97 The Remarkable Versatility of Benford's Law
423(2)
Section 6: Topics in Benford's Law 425(84)
98 Singularities in Exponential Growth Series
427(12)
99 Super Exponential Growth Series
439(3)
100 Higher-Order Leading Digits
442(10)
101 Digit Distributions Assuming Other Bases 450
102 Chains of Distributions Revisited
452(10)
103 Chainable Distributions and Parameters
462(9)
104 Frank Benford's Averaging Scheme as a Distribution Chain
471(3)
105 Effects of Parametrical Transformations on Leading Digits
474(5)
106 Digits of the Wald, Weibull, chi-square, and Gamma Distributions
479(1)
107 Digital Patterns of the Exponential Distribution
480(17)
108 Saville Regression Measure Revisited 484-
109 The Scale Invariance Principle and AGD Interpretation
497(4)
110 Case Study XI: Large Sample from a Variety of Data Sources
501(5)
111 Direct Expression of first Digit for any Number - Computer Use
506(1)
112 Artificially Creating Nearly Perfect Logarithmic Data
507(2)
Section 7: The Law of Relative Quantities 509(128)
113 The Relating Concepts of Digits, Numbers, and Quantities
511(2)
114 Benford's Law in its Purest Form
513(6)
115 Number System Invariance Principle
519(3)
116 Cartesian Coordinate System is Number-System-Invariant
522(2)
117 Physics is Number-System-Invariant
524(3)
118 Multiplicative CLT is Number-System-Invariant
527(2)
119 Greek Parable and Chains are Number-System-Invariant
529(1)
120 Physical Reality versus Digital Perception
530(1)
121 Patterns in Physical Data Transcend Number Systems and Digits
531(1)
122 Common Thread Going Through Multiple Physical Data Sets
532(3)
123 Casting a Repetitive Bin System to Measure Fall in Histogram
535(7)
124 Non-Expanding Bin System Measuring Fall in k/x Distribution
542(5)
125 Once-Expanding Bin System Measuring Fall in k/x Distribution
547(2)
126 Once-Expanding Bins for k/x Reduces to Benford when F = D + 1
549(1)
127 Twice-Expanding Bin System Measuring Fall in k/x Distribution
550(2)
128 Twice-Expanding Bins for k/x Reduces to Benford when F = D + 1
552(2)
129 Infinitely Expanding Bin System Measuring Fall in k/x
554(2)
130 Confirmation Matching k/x Fall with Empirical Bins on Real Data
556(2)
131 Closed Form Expression for the Limit of the Infinite Sequence
558(5)
132 Closed Form Expression for the Limit in the Flat Case F = 1
563(4)
133 9-Bin Systems with F = 10 on Real Data All Yield LOGTEN(1+1/d)
567(2)
134 Bin Systems Need to Start Near Origin with Small Initial Width
569(6)
135 Actual or Degree of Compliance May Be Bin- and Base-Variant
575(7)
136 Correspondence in Data Classification Between Bin Systems and BL
582(8)
137 F = D + 1 Bin Systems on Real DataYield LOGBAsE(1+1/d)
590(1)
138 The Remarkable Malleability and Universality of Bin Schemes
591(11)
139 Higher-Order Digits Interpreted as Particular Bin Schemes
602(6)
140 Bin Development Pattern
608(6)
141 The General Scale Invariance Principle
614(5)
142 Paradoxes Explained
619(3)
143 Digits Serving as Quantities in Benford's Law
622(2)
144 Frank Benford's Prophetic Words
624(1)
145 Future Direction
625(1)
146 The Universal Law of Relative Quantities
626(2)
147 Dialogue Concerning the Two Chief Statistical Systems
628(9)
References 637(6)
Glossary of Frequently Used Abbreviations 643(2)
Index 645