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Data Analytics for Internal Auditors.

By: Cascarino, Richard E.
Material type: TextTextSeries: eBooks on Demand.Internal Audit and IT Audit: Publisher: Boca Raton : CRC Press, 2017Copyright date: ©2017Description: 1 online resource (439 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9781498737159.Subject(s): Auditing, InternalGenre/Form: Electronic books.Additional physical formats: Print version:: Data Analytics for Internal AuditorsDDC classification: 657.458 Online resources: Click here to view this ebook.
Contents:
Half Title -- Title Page -- Copyright Page -- Table of Contents -- About the Author -- Introduction -- Data Analytics for Internal Auditors -- A Practitioner's Handbook -- Book Contents -- Chapter 1: Introduction to Data Analysis -- Chapter 2: Understanding Sampling -- Chapter 3: Judgmental versus Statistical Sampling -- Chapter 4: Probability Theory in Data Analysis -- Chapter 5: Types of Evidence -- Chapter 6: Population Analysis -- Chapter 7: Correlations, Regressions, and Other Analyses -- Chapter 8: Conducting the Audit -- Chapter 9: Obtaining Information from IT Systems for Analysis -- Chapter 10: Use of Computer-Assisted Audit Techniques -- Chapter 11: Analysis of Big Data -- Chapter 12: Results Analysis and Validation -- Chapter 13: Fraud Detection Using Data Analysis -- Chapter 14: Root Cause Analysis -- Chapter 15: Data Analysis and Continuous Monitoring -- Chapter 16: Continuous Auditing -- Chapter 17: Financial Analysis -- Chapter 18: Excel and Data Analysis -- Chapter 19: ACL and Data Analysis -- Chapter 20: IDEA and Data Analysis -- Chapter 21: SAS and Data Analysis -- Chapter 22: Analysis Reporting -- Chapter 23: Data Visualization and Presentation -- Appendix 1: ACL Usage -- Appendix 2: IDEA Usage -- Appendix 3: Risk Assessment: A Working Example -- The Cascarino Cube -- Chapter 1: Introduction to Data Analysis -- Benefits to Audit -- Data Classification -- Audit Analytical Techniques -- Data Modeling -- Data Input Validation -- Getting the Right Data for Analysis -- Statistics -- Chapter 2: Understanding Sampling -- Population Sampling -- Sampling Risk -- General Advantages -- Planning the Audit -- Data Analysis Objectives -- Characteristics of Populations -- Population Variability and Probability Distributions -- Binomial Distributions -- Poisson Distribution -- Continuous Probability Distributions -- Normal Distribution.
Uniform Distributions -- Exponential Distribution -- Central Tendency and Skewed Distributions -- Population Characteristics -- Chapter 3: Judgmental versus Statistical Sampling -- Judgmental Sampling -- The Statistical Approach -- Sampling Methods -- Calculation of Sample Sizes -- Attribute Sampling Formula -- Classic Variable Sampling Formula -- PPS Sampling Formula -- Selecting the Sample -- Interpreting the Results -- Nonparametric Testing -- Confusing Judgmental and Statistical Sampling -- Common Statistical Errors -- Chapter 4: Probability Theory in Data Analysis -- Probability Definitions -- Classical Probability -- Empirical Probability -- Subjective Probability -- Probability Multiplication -- Conditional Probability -- Bayes' Theorem -- Use in Audit Risk Evaluation -- Other Uses -- Financial Auditing -- Overstatement of Assets -- Probability Distributions -- Chapter 5: Types of Evidence -- Influencing Factors -- Quantity Required -- Reliability of Evidence -- Relevance of Evidence -- Management Assertions -- Audit Procedures -- Documenting the Audit Evidence -- Working Papers -- Working Paper Types Working papers for an audit are made up of two general types of files. The first type of file is the permanent file, which contains all the relevant information that may be of interest during future audits. The second type of file is t -- Contents of Permanent File -- Contents of Current File -- Selection -- Client Background -- Internal Control Descriptions -- Audit Program -- Results of Audit Tests -- Audit Comment Worksheets -- Report Planning Worksheets -- Copy of the Audit Report -- Follow-Up Program -- Follow-Up of Prior Audit Findings -- Audit Evaluation -- Ongoing Concerns -- Administrative/Correspondence -- General Standards of Completion -- Cross-Referencing -- Tick Marks -- Notes -- Working Paper Review.
General Review Considerations -- Working Paper Retention/Security -- Chapter 6: Population Analysis -- Types of Data -- Correspondence Analysis -- Factor Analysis -- Populations -- Sampling Error -- Central Tendency -- Variation -- Shape of Curve -- Chapter 7: Correlations, Regressions, and Other Analyses -- Quantitative Methods -- Trend Analysis -- Chi-Squared Tests -- Correspondence Analysis -- Cluster Analysis -- Graphical Analysis -- Correlation Analysis -- Audit Use of Correlation Analysis -- Learning Curves -- Ratio and Regression Analysis -- The Least Squares Regression Line -- Audit Use of Regression Analysis -- Linear Programming -- Parametric Assumptions -- Nonparametric Measurement -- Kruskal-Wallis Analysis of Variance (ANOVA) Testing -- Chapter 8: Conducting the Audit -- Audit Planning -- Risk Analysis -- Determining Audit Objectives -- Compliance Audits -- Environmental Audits -- Financial Audits -- Performance and Operational Audits -- Fraud Audits -- Forensic Auditing -- Quality Audits -- Program Results Audits -- IT Audits -- Audits of Significant Balances and Classes of Transactions -- Accounts Payable Audits -- Accounts Receivable Audits -- Payroll Audits -- Banking Treasury Audits -- Corporate Treasury Audits -- Chapter 9: Obtaining Information from IT Systems for Analysis -- Data Representation -- Binary and Hexadecimal Data -- Binary System All computer systems store data in binary mode (Base 2). Unlike the decimal system that has 10 digits from zero to nine, the binary system has only two digits: zero and one. -- Hexadecimal System Although all computer data is in binary form, this data is represented in practice in hexadecimal mode (Base 16), which is compact and easy to handle. For example, the number 65 in the decimal system is equivalent to the binary number 0 -- ASCII and EBCDIC.
Fixed-Length Data Fixed-length files consist of records that have a fixed length, which will always occupy the same space and contain the same number of characters. In this type of file, the same individual data elements or fields will be present. Fields, -- Delimited Data In this case, although all records again have the same individual fields, the fields are of variable lengths with the start and end of such fields indicated by commas, semicolons, quotation marks, and the like. -- Variable-Length Data There are occasions when the auditor may encounter records of variable lengths stored in a single file: -- Databases -- Definition of Terms -- Principals of Data Structures -- Database Structuring Approaches -- Sequential or Flat File Approach In this form, data is stored in the form of one or more data files, which are nothing but simple text files (ASCII or EBCDIC), which can be viewed directly using a text editor and printed without difficulty (Diagram 9.1). -- Hierarchical Approach -- Network Approach -- Relational Model -- Data Manipulation -- Terminology -- Big Data -- The Download Process -- Access to Data -- Downloading Data -- Data Verification -- Obtaining Data from Printouts -- Sanitization of Data -- Documenting the Download -- Chapter 10: Use of Computer-Assisted Audit Techniques -- Use of CAATs -- Standards of Evidence -- Test Techniques -- Embedded Audit Modules (SCARFs- System ​Control Audit Review Files) -- CAATs for Data Analysis -- Generalized Audit Software -- Application- and Industry-Related Audit Software -- Customized Audit Software -- Information Retrieval Software -- Utilities -- Conventional Programming Languages -- Common Problems -- Audit Procedures -- CAAT Use in Non-Computerized Areas -- Getting Started -- CAAT Usage -- Finance and Banking -- Government -- Retail -- Services and Distribution -- Health Care.
General Accounting Analyses -- Chapter 11: Analysis of Big Data -- Online Analytical Processing (OLAP) -- Big Data Structures -- Other Big Data Technologies -- Hive -- Statistical Analysis and Big Data -- R -- Chapter 12: Results Analysis and Validation -- Implementation of the Audit Plan -- Substantive Analytical Procedures -- Validation -- Data Selection Bias -- Questionnaire Analysis -- Use of Likert Scales in Data Analysis -- Statistical Reliability Analysis -- Chapter 13: Fraud Detection Using Data Analysis -- Red Flags and Indicators -- Pressure Sources -- Changes in Behavior -- General Personality Traits -- Nature of Computer Fraud -- Computer Fraud Protection -- Cloud Computing -- Information Fraud -- Seeking Fraud Evidence -- Chain of Custody -- Starting the Process -- Detecting e-Commerce Fraud -- Business-to-Consumer (B2C) -- Business-to-Business (B2B) -- Fraud Detection in the Cloud -- Planning the Fraud Analysis -- Common Mistakes in Forensic Analysis -- Chapter 14: Root Cause Analysis -- Chapter 15: Data Analysis and Continuous Monitoring -- Monitoring Tools -- Software Vendors -- Implementing Continuous Monitoring -- Overcoming Negative Perceptions -- Potential Benefits -- Chapter 16: Continuous Auditing -- Continuous Auditing as Opposed to Continuous Monitoring -- Implementing Continuous Auditing -- Structuring the Implementation -- Perceived Downsides of Continuous Auditing -- Actual Challenges -- Obtaining Support -- Maintaining the Support -- Chapter 17: Financial Analysis -- Analyzing Financial Data -- Balance Sheet -- Income Statement -- Statement of Cash Flows -- Creative Revenue Enhancements -- Depreciation Assumptions -- Extraordinary Gains and Losses -- Use of Ratios -- Horizontal Analysis -- Vertical Analysis -- DuPont Analysis -- Subsidiary Ledgers -- Accounts Payable Analysis and Reconciliation.
Analysis of Duplicate Payments.
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
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HF5668.C37 2017 (Browse shelf) https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=4824768 Available EBC4824768

Half Title -- Title Page -- Copyright Page -- Table of Contents -- About the Author -- Introduction -- Data Analytics for Internal Auditors -- A Practitioner's Handbook -- Book Contents -- Chapter 1: Introduction to Data Analysis -- Chapter 2: Understanding Sampling -- Chapter 3: Judgmental versus Statistical Sampling -- Chapter 4: Probability Theory in Data Analysis -- Chapter 5: Types of Evidence -- Chapter 6: Population Analysis -- Chapter 7: Correlations, Regressions, and Other Analyses -- Chapter 8: Conducting the Audit -- Chapter 9: Obtaining Information from IT Systems for Analysis -- Chapter 10: Use of Computer-Assisted Audit Techniques -- Chapter 11: Analysis of Big Data -- Chapter 12: Results Analysis and Validation -- Chapter 13: Fraud Detection Using Data Analysis -- Chapter 14: Root Cause Analysis -- Chapter 15: Data Analysis and Continuous Monitoring -- Chapter 16: Continuous Auditing -- Chapter 17: Financial Analysis -- Chapter 18: Excel and Data Analysis -- Chapter 19: ACL and Data Analysis -- Chapter 20: IDEA and Data Analysis -- Chapter 21: SAS and Data Analysis -- Chapter 22: Analysis Reporting -- Chapter 23: Data Visualization and Presentation -- Appendix 1: ACL Usage -- Appendix 2: IDEA Usage -- Appendix 3: Risk Assessment: A Working Example -- The Cascarino Cube -- Chapter 1: Introduction to Data Analysis -- Benefits to Audit -- Data Classification -- Audit Analytical Techniques -- Data Modeling -- Data Input Validation -- Getting the Right Data for Analysis -- Statistics -- Chapter 2: Understanding Sampling -- Population Sampling -- Sampling Risk -- General Advantages -- Planning the Audit -- Data Analysis Objectives -- Characteristics of Populations -- Population Variability and Probability Distributions -- Binomial Distributions -- Poisson Distribution -- Continuous Probability Distributions -- Normal Distribution.

Uniform Distributions -- Exponential Distribution -- Central Tendency and Skewed Distributions -- Population Characteristics -- Chapter 3: Judgmental versus Statistical Sampling -- Judgmental Sampling -- The Statistical Approach -- Sampling Methods -- Calculation of Sample Sizes -- Attribute Sampling Formula -- Classic Variable Sampling Formula -- PPS Sampling Formula -- Selecting the Sample -- Interpreting the Results -- Nonparametric Testing -- Confusing Judgmental and Statistical Sampling -- Common Statistical Errors -- Chapter 4: Probability Theory in Data Analysis -- Probability Definitions -- Classical Probability -- Empirical Probability -- Subjective Probability -- Probability Multiplication -- Conditional Probability -- Bayes' Theorem -- Use in Audit Risk Evaluation -- Other Uses -- Financial Auditing -- Overstatement of Assets -- Probability Distributions -- Chapter 5: Types of Evidence -- Influencing Factors -- Quantity Required -- Reliability of Evidence -- Relevance of Evidence -- Management Assertions -- Audit Procedures -- Documenting the Audit Evidence -- Working Papers -- Working Paper Types Working papers for an audit are made up of two general types of files. The first type of file is the permanent file, which contains all the relevant information that may be of interest during future audits. The second type of file is t -- Contents of Permanent File -- Contents of Current File -- Selection -- Client Background -- Internal Control Descriptions -- Audit Program -- Results of Audit Tests -- Audit Comment Worksheets -- Report Planning Worksheets -- Copy of the Audit Report -- Follow-Up Program -- Follow-Up of Prior Audit Findings -- Audit Evaluation -- Ongoing Concerns -- Administrative/Correspondence -- General Standards of Completion -- Cross-Referencing -- Tick Marks -- Notes -- Working Paper Review.

General Review Considerations -- Working Paper Retention/Security -- Chapter 6: Population Analysis -- Types of Data -- Correspondence Analysis -- Factor Analysis -- Populations -- Sampling Error -- Central Tendency -- Variation -- Shape of Curve -- Chapter 7: Correlations, Regressions, and Other Analyses -- Quantitative Methods -- Trend Analysis -- Chi-Squared Tests -- Correspondence Analysis -- Cluster Analysis -- Graphical Analysis -- Correlation Analysis -- Audit Use of Correlation Analysis -- Learning Curves -- Ratio and Regression Analysis -- The Least Squares Regression Line -- Audit Use of Regression Analysis -- Linear Programming -- Parametric Assumptions -- Nonparametric Measurement -- Kruskal-Wallis Analysis of Variance (ANOVA) Testing -- Chapter 8: Conducting the Audit -- Audit Planning -- Risk Analysis -- Determining Audit Objectives -- Compliance Audits -- Environmental Audits -- Financial Audits -- Performance and Operational Audits -- Fraud Audits -- Forensic Auditing -- Quality Audits -- Program Results Audits -- IT Audits -- Audits of Significant Balances and Classes of Transactions -- Accounts Payable Audits -- Accounts Receivable Audits -- Payroll Audits -- Banking Treasury Audits -- Corporate Treasury Audits -- Chapter 9: Obtaining Information from IT Systems for Analysis -- Data Representation -- Binary and Hexadecimal Data -- Binary System All computer systems store data in binary mode (Base 2). Unlike the decimal system that has 10 digits from zero to nine, the binary system has only two digits: zero and one. -- Hexadecimal System Although all computer data is in binary form, this data is represented in practice in hexadecimal mode (Base 16), which is compact and easy to handle. For example, the number 65 in the decimal system is equivalent to the binary number 0 -- ASCII and EBCDIC.

Fixed-Length Data Fixed-length files consist of records that have a fixed length, which will always occupy the same space and contain the same number of characters. In this type of file, the same individual data elements or fields will be present. Fields, -- Delimited Data In this case, although all records again have the same individual fields, the fields are of variable lengths with the start and end of such fields indicated by commas, semicolons, quotation marks, and the like. -- Variable-Length Data There are occasions when the auditor may encounter records of variable lengths stored in a single file: -- Databases -- Definition of Terms -- Principals of Data Structures -- Database Structuring Approaches -- Sequential or Flat File Approach In this form, data is stored in the form of one or more data files, which are nothing but simple text files (ASCII or EBCDIC), which can be viewed directly using a text editor and printed without difficulty (Diagram 9.1). -- Hierarchical Approach -- Network Approach -- Relational Model -- Data Manipulation -- Terminology -- Big Data -- The Download Process -- Access to Data -- Downloading Data -- Data Verification -- Obtaining Data from Printouts -- Sanitization of Data -- Documenting the Download -- Chapter 10: Use of Computer-Assisted Audit Techniques -- Use of CAATs -- Standards of Evidence -- Test Techniques -- Embedded Audit Modules (SCARFs- System ​Control Audit Review Files) -- CAATs for Data Analysis -- Generalized Audit Software -- Application- and Industry-Related Audit Software -- Customized Audit Software -- Information Retrieval Software -- Utilities -- Conventional Programming Languages -- Common Problems -- Audit Procedures -- CAAT Use in Non-Computerized Areas -- Getting Started -- CAAT Usage -- Finance and Banking -- Government -- Retail -- Services and Distribution -- Health Care.

General Accounting Analyses -- Chapter 11: Analysis of Big Data -- Online Analytical Processing (OLAP) -- Big Data Structures -- Other Big Data Technologies -- Hive -- Statistical Analysis and Big Data -- R -- Chapter 12: Results Analysis and Validation -- Implementation of the Audit Plan -- Substantive Analytical Procedures -- Validation -- Data Selection Bias -- Questionnaire Analysis -- Use of Likert Scales in Data Analysis -- Statistical Reliability Analysis -- Chapter 13: Fraud Detection Using Data Analysis -- Red Flags and Indicators -- Pressure Sources -- Changes in Behavior -- General Personality Traits -- Nature of Computer Fraud -- Computer Fraud Protection -- Cloud Computing -- Information Fraud -- Seeking Fraud Evidence -- Chain of Custody -- Starting the Process -- Detecting e-Commerce Fraud -- Business-to-Consumer (B2C) -- Business-to-Business (B2B) -- Fraud Detection in the Cloud -- Planning the Fraud Analysis -- Common Mistakes in Forensic Analysis -- Chapter 14: Root Cause Analysis -- Chapter 15: Data Analysis and Continuous Monitoring -- Monitoring Tools -- Software Vendors -- Implementing Continuous Monitoring -- Overcoming Negative Perceptions -- Potential Benefits -- Chapter 16: Continuous Auditing -- Continuous Auditing as Opposed to Continuous Monitoring -- Implementing Continuous Auditing -- Structuring the Implementation -- Perceived Downsides of Continuous Auditing -- Actual Challenges -- Obtaining Support -- Maintaining the Support -- Chapter 17: Financial Analysis -- Analyzing Financial Data -- Balance Sheet -- Income Statement -- Statement of Cash Flows -- Creative Revenue Enhancements -- Depreciation Assumptions -- Extraordinary Gains and Losses -- Use of Ratios -- Horizontal Analysis -- Vertical Analysis -- DuPont Analysis -- Subsidiary Ledgers -- Accounts Payable Analysis and Reconciliation.

Analysis of Duplicate Payments.

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