This uniquely accessible, breakthrough book lets auditors grasp the
thinking behind the mathematical approach to risk without
doing the mathematics.
Risk control expert and former Big 4 auditor, Matthew Leitch,
takes the reader gently but quickly through the key concepts,
explaining mistakes organizations often make and how auditors can
find them.
Spend a few minutes every day reading this conveniently pocket
sized book and you will soon transform your understanding of this
highly topical area and be in demand for interesting reviews with
risk at their heart.
"I was really excited by this book - and I am not a
mathematician. With my basic understanding of business statistics
and business risk management I was able to follow the arguments
easily and pick up the jargon of a discipline akin to my own but
not my own."
--Dr Sarah Blackburn, President at the Institute of
Internal Auditors - UK and Ireland
Autorentext
Matthew Leitch (Epsom, UK) is an author on a mission to make risk control easier, more natural, and much more valuable. His insightful, readable books are at the leading edge of thinking and practice in internal control and risk management. He frequently carries out original research on topical questions, such as how our use of words affects the way we think about uncertainty, and what expertise auditors need. He is a qualified chartered accountant and holds a BSc in psychology from University College London. He is author of Intelligent Internal Control and Risk Management, and runs the website, www.internalcontrolsdesign.co.uk. He speaks at numerous risk and audit conferences for organizations including the IIA and IIR.
Zusammenfassung
This uniquely accessible, breakthrough book lets auditors grasp the thinking behind the mathematical approach to risk without doing the mathematics.
Risk control expert and former Big 4 auditor, Matthew Leitch, takes the reader gently but quickly through the key concepts, explaining mistakes organizations often make and how auditors can find them.
Spend a few minutes every day reading this conveniently pocket sized book and you will soon transform your understanding of this highly topical area and be in demand for interesting reviews with risk at their heart.
"I was really excited by this book - and I am not a mathematician. With my basic understanding of business statistics and business risk management I was able to follow the arguments easily and pick up the jargon of a discipline akin to my own but not my own."
Dr Sarah Blackburn, President at the Institute of Internal Auditors - UK and Ireland
Inhalt
Start here 1
Good choice! 1
This book 2
How this book works 3
The myth of mathematical clarity 5
The myths of quantification 7
The auditor's mission 8
Auditing simple risk assessments 11
1 Probabilities 12
2 Probabilistic forecaster 13
3 Calibration (also known as reliability) 13
4 Resolution 14
5 Proper score function 15
6 Audit point: Judging probabilities 17
7 Probability interpretations 17
8 Degree of belief 18
9 Situation (also known as an experiment) 19
10 Long run relative frequency 20
11 Degree of belief about long run relative frequency 21
12 Degree of belief about an outcome 22
13 Audit point: Mismatched interpretations of probability 24
14 Audit point: Ignoring uncertainty about probabilities 25
15 Audit point: Not using data to illuminate probabilities 25
16 Outcome space (also known as sample space, or possibility space) 26
17 Audit point: Unspecified situations 27
18 Outcomes represented without numbers 28
19 Outcomes represented with numbers 29
20 Random variable 29
21 Event 30
22 Audit point: Events with unspecified boundaries 31
23 Audit point: Missing ranges 32
24 Audit point: Top 10 risk reporting 32
25 Probability of an outcome 33
26 Probability of an event 34
27 Probability measure (also known as probability distribution, probability function, or even probability distribution function) 34
28 Conditional probabilities 36
29 Discrete random variables 37
30 Continuous random variables 38
31 Mixed random variables (also known as mixed discrete-continuous random variables) 39
32 Audit point: Ignoring mixed random variables 40
33 Cumulative probability distribution function 41
34 Audit point: Ignoring impact spread 43
35 Audit point: Confusing money and utility 44
36 Probability mass function 44
37 Probability density function 45
38 Sharpness 47
39 Risk 49
40 Mean value of a probability distribution (also known as the expected value) 50
41 Audit point: Excessive focus on expected values 51
42 Audit point: Misunderstanding 'expected' 51
43 Audit point: Avoiding impossible provisions 52
44 Audit point: Probability impact matrix numbers 53
45 Variance 54
46 Standard deviation 55
47 Semi-variance 55
48 Downside probability 55
49 Lower partial moment 56
50 Value at risk (VaR) 56
51 Audit point: Probability times impact 58
Some types of probability distribution 61
52 Discrete uniform distribution 62
53 Zipf distribution 62
54 Audit point: Benford's law 64
55 Non-parametric distributions 65
56 Analytical expression 65
57 Closed form (also known as a closed formula or explicit formula) 66
58 Categorical distribution 67
59 Bernoulli distribution 67
60 Binomial distribution 68
61 Poisson distribution 69
62 Multinomial distribution 70
63 Continuous uniform distribution 70
64 Pareto distribution and power law distribution 71
65 Triangular distribution 73
66 Normal distribution (also known as the Gaussian distribution) 74
67 Audit point: Normality tests 77
68 Non-parametric continuous distributions 78
69 Audit point: Multi-modal distributions 78
70 Lognormal distribution 79
71 Audit point: Thin tails 80
72 Joint distribution 80
73 Joint normal distribution 81
74 Beta distribution 82
Auditing the design of business prediction models 83
75 Process (also known as a system) 84
76 Population 84
77 Mathematical model 85
78 Audit point: Mixing models and registers 86
79 Probabilistic models (also known as stochastic models or statistical models) 86
80 Model structure 88
81 Audit point: Lost assumptions 89
82 Prediction formulae 89
83 Simulations 90
84 Optimization 90
85 Model inputs 90
86 Prediction formula structure 91
87 Numerical equation solving 93
88 Prediction algorithm 94
89 Prediction errors 94
90 Model uncertainty 94
91 Audit point: Ignoring model uncertainty 95
92 Measurement uncertainty 96
93 Audit point: Ignoring measurement uncertainty 96
94 Audit point: Best guess forecasts 97
95 Prediction intervals 97
96 Propagating uncertainty 98
97 Audit point: The flaw of averages 99
98 Random 100
99 Theoretically random 101
100 Real life random 102
101 Audit point: Fooled by randomness (1) 102
102 Audit point: Fooled by randomness (2) 104
103 Pseudo random number generation 104
104 Monte Carlo simulation 105
105 Audit point: Ignoring real options 109
106 Tornado diagram 109
107 Audit point: Guessing impact 111
108 Conditional dependence and independence 112
109 Correlation (also known as linea…