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E-raamat: Data Analytics for Internal Auditors

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There are many webinars and training courses on Data Analytics for Internal Auditors, but no handbook written from the practitioner’s viewpoint covering not only the need and the theory, but a practical hands-on approach to conducting Data Analytics. The spread of IT systems makes it necessary that auditors as well as management have the ability to examine high volumes of data and transactions to determine patterns and trends. The increasing need to continuously monitor and audit IT systems has created an imperative for the effective use of appropriate data mining tools. This book takes an auditor from a zero base to an ability to professionally analyze corporate data seeking anomalies.

About The Author xiii
Introduction xv
Chapter 1 Introduction To Data Analysis 1(14)
Benefits to Audit
2(3)
Data Classification
5(2)
Audit Analytical Techniques
7(1)
Data Modeling
7(1)
Data Input Validation
8(1)
Getting the Right Data for Analysis
9(2)
Statistics
11(4)
Chapter 2 Understanding Sampling 15(14)
Population Sampling
15(2)
Sampling Risk
17(3)
General Advantages
20(1)
Planning the Audit
20(1)
Data Analysis Objectives
21(1)
Characteristics of Populations
21(1)
Population Variability and Probability Distributions
22(2)
Binomial Distributions
22(1)
Poisson Distribution
23(1)
Continuous Probability Distributions
24(3)
Normal Distribution
24(1)
Uniform Distributions
25(1)
Exponential Distribution
26(1)
Central Tendency and Skewed Distributions
26(1)
Population Characteristics
27(2)
Chapter 3 Judgmental Versus Statistical Sampling 29(16)
Judgmental Sampling
29(2)
The Statistical Approach
31(1)
Sampling Methods
31(5)
Calculation of Sample Sizes
36(4)
Attribute Sampling Formula
36(2)
Classic Variable Sampling Formula
38(1)
PPS Sampling Formula
38(2)
Selecting the Sample
40(1)
Interpreting the Results
41(1)
Nonparametric Testing
42(1)
Confusing Judgmental and Statistical Sampling
43(1)
Common Statistical Errors
43(2)
Chapter 4 Probability Theory In Data Analysis 45(10)
Probability Definitions
45(1)
Classical Probability
46(1)
Empirical Probability
47(1)
Subjective Probability
48(1)
Probability Multiplication
48(1)
Conditional Probability
48(2)
Bayes' Theorem
50(1)
Use in Audit Risk Evaluation
51(1)
Other Uses
52(1)
Financial Auditing
52(1)
Overstatement of Assets
53(1)
Probability Distributions
53(2)
Chapter 5 Types Of Evidence 55(16)
Influencing Factors
55(4)
Quantity Required
57(1)
Reliability of Evidence
57(1)
Relevance of Evidence
58(1)
Management Assertions
58(1)
Audit Procedures
59(1)
Documenting the Audit Evidence
60(2)
Working Papers
60(3)
Working Paper Types
60(2)
Contents of Permanent File
62(1)
Contents of Current File
63(3)
Selection
63(1)
Client Background
63(1)
Internal Control Descriptions
64(1)
Audit Program
64(1)
Results of Audit Tests
64(1)
Audit Comment Worksheets
65(1)
Report Planning Worksheets
65(1)
Copy of the Audit Report
65(1)
Follow-Up Program
65(1)
Follow-Up of Prior Audit Findings
66(1)
Audit Evaluation
66(1)
Ongoing Concerns
66(1)
Administrative/Correspondence
66(1)
General Standards of Completion
66(5)
Cross-Referencing
66(1)
Tick Marks
67(1)
Notes
68(1)
Working Paper Review
68(1)
General Review Considerations
69(1)
Working Paper Retention/Security
70(1)
Chapter 6 Population Analysis 71(12)
Types of Data
71(1)
Correspondence Analysis
72(1)
Factor Analysis
72(2)
Populations
74(1)
Sampling Error
75(1)
Central Tendency
76(1)
Variation
77(3)
Shape of Curve
80(3)
Chapter 7 Correlations, Regressions, And Other Analyses 83(16)
Quantitative Methods
83(1)
Trend Analysis
83(2)
Chi-Squared Tests
85(1)
Correspondence Analysis
86(1)
Cluster Analysis
86(2)
Graphical Analysis
88(1)
Correlation Analysis
88(2)
Audit Use of Correlation Analysis
90(1)
Learning Curves
91(1)
Ratio and Regression Analysis
92(1)
The Least Squares Regression Line
93(1)
Audit Use of Regression Analysis
94(1)
Linear Programming
94(2)
Parametric Assumptions
96(1)
Nonparametric Measurement
96(1)
Kruskal-Wallis Analysis of Variance (ANOVA) Testing
96(3)
Chapter 8 Conducting The Audit 99(20)
Audit Planning
99(1)
Risk Analysis
100(4)
Determining Audit Objectives
104(1)
Compliance Audits
105(1)
Environmental Audits
106(1)
Financial Audits
106(1)
Performance and Operational Audits
107(1)
Fraud Audits
107(1)
Forensic Auditing
108(2)
Quality Audits
110(1)
Program Results Audits
110(1)
IT Audits
111(1)
Audits of Significant Balances and Classes of Transactions
112(7)
Accounts Payable Audits
114(1)
Accounts Receivable Audits
115(1)
Payroll Audits
116(1)
Banking Treasury Audits
116(1)
Corporate Treasury Audits
117(2)
Chapter 9 Obtaining Information From It Systems For Analysis 119(16)
Data Representation
119(7)
Binary and Hexadecimal Data
119(1)
Binary System
119(1)
Hexadecimal System
119(1)
ASCII and EBCDIC
120(1)
Fixed-Length Data
120(1)
Delimited Data
121(1)
Variable-Length Data
121(1)
Databases
121(1)
Definition of Terms
122(1)
Principals. of Data Structures
123(1)
Database Structuring Approaches
123(2)
Sequential or Flat File Approach
123(1)
Hierarchical Approach
124(1)
Network Approach
124(1)
Relational Model
125(1)
Data Manipulation
125(24)
Terminology
126(1)
Big Data
126(2)
The Download Process
128(1)
Access to Data
129(1)
Downloading Data
129(1)
Data Verification
130(1)
Obtaining Data from Printouts
131(1)
Sanitization of Data
131(1)
Documenting the Download
132(3)
Chapter 10 Use Of Computer-Assisted Audit Techniques 135(24)
Use of CAATs
135(1)
Standards of Evidence
136(1)
Test Techniques
137(2)
Embedded Audit Modules (SCARFs-System Control Audit Review Files)
139(1)
CAATs for Data Analysis
139(2)
Generalized Audit Software
141(2)
Application- and Industry-Related Audit Software
143(1)
Customized Audit Software
144(1)
Information Retrieval Software
144(1)
Utilities
144(1)
Conventional Programming Languages
144(1)
Common Problems
145(1)
Audit Procedures
146(1)
CAAT Use in Non-Computerized Areas
147(1)
Getting Started
147(2)
CAAT Usage
149(8)
Finance and Banking
150(1)
Government
151(3)
Retail
154(1)
Services and Distribution
155(1)
Health Care
155(2)
General Accounting Analyses
157(2)
Chapter 11 Analysis Of Big Data 159(12)
Online Analytical Processing (OLAP)
161(1)
Big Data Structures
162(2)
Other Big Data Technologies
164(3)
Hive
167(1)
Statistical Analysis and Big Data
167(1)
R
168(3)
Chapter 12 Results Analysis And Validation 171(10)
Implementation of the Audit Plan
172(1)
Substantive Analytical Procedures
173(2)
Validation
175(2)
Data Selection Bias
177(1)
Questionnaire Analysis
177(1)
Use of Likert Scales in Data Analysis
178(1)
Statistical Reliability Analysis
179(2)
Chapter 13 Fraud Detection Using Data Analysis 181(24)
Red Flags and Indicators
181(3)
Pressure Sources
181(1)
Changes in Behavior
182(1)
General Personality Traits
182(2)
Nature of Computer Fraud
184(1)
Computer Fraud Protection
185(1)
Cloud Computing
186(1)
Information Fraud
187(1)
Seeking Fraud Evidence
188(1)
Chain of Custody
189(1)
Starting the Process
190(8)
Detecting e-Commerce Fraud
198(3)
Business-to-Consumer (B2C)
200(1)
Business-to-Business (B2B)
200(1)
Fraud Detection in the Cloud
201(1)
Planning the Fraud Analysis
202(1)
Common Mistakes in Forensic Analysis
203(2)
Chapter 14 Root Cause Analysis 205(6)
Chapter 15 Data Analysis And Continuous Monitoring 211(14)
Monitoring Tools
216(2)
Software Vendors
218(2)
Implementing Continuous Monitoring
220(3)
Overcoming Negative Perceptions
223(1)
Potential Benefits
224(1)
Chapter 16 Continuous Auditing 225(12)
Continuous Auditing as Opposed to Continuous Monitoring
225(2)
Implementing Continuous Auditing
227(1)
Structuring the Implementation
228(2)
Perceived Downsides of Continuous Auditing
230(2)
Actual Challenges
232(1)
Obtaining Support
233(1)
Maintaining the Support
234(3)
Chapter 17 Financial Analysis 237(26)
Analyzing Financial Data
238(8)
Balance Sheet
240(2)
Income Statement
242(1)
Statement of Cash Flows
243(3)
Creative Revenue Enhancements
246(1)
Depreciation Assumptions
246(1)
Extraordinary Gains and Losses
246(1)
Use of Ratios
247(5)
Horizontal Analysis
252(1)
Vertical Analysis
252(1)
DuPont Analysis
252(2)
Subsidiary Ledgers
254(2)
Accounts Payable Analysis and Reconciliation
256(1)
Analysis of Duplicate Payments
257(1)
Payments for Goods or Services Not Received
257(1)
Financial Database Analysis
258(1)
Achieving Appropriate Discounts
259(1)
Analyzing Accounts Receivable
259(4)
Chapter 18 Excel And Data Analysis 263(12)
Excel Data Acquisition
265(3)
Excel Functions
266(1)
Excel Database Functions
266(1)
Excel Financial Functions
267(1)
Financial Analysis Using Excel
268(1)
DuPont Analysis
268(1)
Z Score Analysis
269(1)
Graphing and Charting
270(1)
ACL Add-On
271(4)
Chapter 19 ACL And Data Analysis 275(12)
Access to Data
275(1)
Importing Data into ACL
276(3)
Joining and Merging Tables
279(1)
Starting the Analysis
280(1)
Analysis Options
280(2)
ACL Tools
282(2)
ACL Scripts
282(1)
ACL Script Editor
283(1)
Script Recorder
283(1)
Creating from a Table History
283(1)
Creating from Log Entries
284(1)
Exporting a Script
284(1)
Copying from Another ACL Project
284(1)
Continuous Monitoring/Auditing in ACL
284(1)
Data Visualization
285(2)
Chapter 20 Idea And Data Analysis 287(10)
CaseWare IDEA®
287(1)
General Usage
288(1)
Sampling
289(1)
Excel
290(1)
Access
291(1)
Print Report and Adobe PDF Files
291(2)
Text Files
293(4)
Chapter 21 SAS And Data Analysis 297(6)
Operating Environment
298(1)
Importing and Analyzing Data
299(2)
SAS Usage
301(2)
SAS and Fraud Detection
301(1)
Enterprise Case Management
301(2)
Chapter 22 Analysis Reporting 303(18)
Conventional Internal Audit Report Writing
303(3)
Audit Reporting
304(1)
General Audit Reporting and Follow-Up
305(1)
Clear Writing Techniques
306(7)
Subheadings
309(1)
Basic Report Structures
309(1)
Executive Summary
309(1)
Background, Scope, and Objectives
310(1)
Summary of Major Findings
310(1)
Audit Opinion
310(1)
Detailed Findings
311(1)
Recommendations
312(1)
The Technical Analytical Report
313(3)
Polishing and Editing the Report
316(1)
Distributing the Report
317(1)
Following Up
318(3)
Chapter 23 Data Visualization And Presentation 321(16)
Communication Modes
321(3)
Choosing Visuals for Impact
324(6)
Non-Quantitative Visualization
330(1)
Big Data Visualization
330(1)
Using Visualizations
331(1)
Choosing the Tool
332(3)
Internal Audit Usage
335(1)
Making Visualization Effective
336(1)
Chapter 24 Conclusion 337(10)
Where Are We Going?
338(2)
What Stays the Same?
340(1)
Skilling-Up-for the Job
340(1)
Specialists or Generalists
341(1)
Centralized or Decentralized
342(1)
Analytical Problems Now and in the Future
343(1)
Getting Hold of the Data
344(3)
Appendix 1: ACL Usage 347(22)
Appendix 2: Idea Usage 369(20)
Appendix 3: Risk Assessment: A Working Example 389(4)
Index 393
Richard E. Cascarino, MBA, CIA, CISM, CFE, CRMA, is well-known in international auditing. He is a principal of Richard Cascarino & Associates, with over 31 years experience in audit training and consultancy. He is a regular speaker to National and International conferences and has presented courses throughout Africa, Europe, the Middle East and the USA.Richard is a Past President of the Institute of Internal Auditors in South Africa, was the founding Regional Director of the Southern African Region of the IIA-Inc and is a member of ISACA, and the Association of Certified Fraud Examiners, where he is a member of the Board of Regents for Higher Education.Richard was Chairman of the Audit Committee of Gauteng cluster 2 (Premier's office, Shared Services and Health) in Johannesburg and is currently the Chairman of the Audit and Risk Committee of the Department of Public Enterprises in South Africa.He is also a visiting Lecturer at the University of the Witwatersrand, author of the book "Internal Auditing: An Integrated Approach," now in its third edition. This book is extensively used as a university textbook worldwide. In addition, he is the author of the "Auditor's Guide to IT Auditing," now in its 2nd edition and "Corporate Fraud and Internal Control: A Framework for Prevention." He is also a contributor to all four editions of QFINANCE, the UItimate Resource.
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