Audit Analytics: Data Science for the Accounting Profession (Use R!)

✍ Scribed by J. Christopher Westland

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 482
• Edition: 2
• Category: Library

✦ Synopsis

This book, using R and RStudio, demonstrates how to render an audit opinion that is legally and statistically defensible; analyze, extract, and manipulate accounting data; build a risk assessment matrix to inform the conduct of a cost-effective audit program; and more.

Today, information technology plays a pivotal role in financial control and audit: most financial data is now digitally recorded and dispersed among servers, clouds and networks over which the audited firm has no control. Additionally, a firm’s data―particularly in the case of finance, software, insurance and biotech firms―comprises most of the audited value of the firm. Financial audits are critical mechanisms for ensuring the integrity of information systems and the reporting of organizational finances. They help avoid the abuses that led to passage of legislation such as the Foreign Corrupt Practices Act (1977), and the Sarbanes-Oxley Act (2002).

Audit effectiveness has declined over the past two decades, as auditor skillsets have failed to keep up with advances in information technology. Information and communication technology lie at the core of commerce today and are integrated in business processes around the world. This book is designed to meet the increasing need of audit professionals to understand information technology and the controls required to manage it. This 2nd edition includes updated code and test. Machine learning, AI, and SEC’s EDGAR data are also, improved and updated.

The material included focuses on the requirements for annual Securities and Exchange Commission audits (10-K) for listed corporations. These represent the benchmark auditing procedures for specialized audits, such as internal, governmental, and attestation audits. Many examples reflect the focus of the 2024 CPA exam, and the data analytics-machine learning approach will be central to the AICPA’s programs, in the near future.

✦ Table of Contents

Foreword
Preface
Contents
1 Fundamentals of Auditing Financial Reports
Radical Changes in the Accounting Profession
Auditing
Computers in Auditing and the Birth of Audit Analytics
The Roots of Modern Financial Accounting and Auditing
Al-Khwarizmı's Algebra of Double-Entry
The Renaissance
The Industrial Revolution
The Birth of Modern Auditing
Public Accounting
Emerging Technologies and Intangible Assets
Financial Accounting
The Balance Sheet
The Income Statement
Cash Flow Statements
The Methodology of Accounting
Generally Accepted Accounting Principles (GAAPs)
Theory
Assumptions
Principles
Constraints
Accounting Entries and Document Files
Books of Accounts
R Packages Required for This Book
References
2 Foundations of Audit Analytics
Business and Data Analytics
Accounting Data Types
Numerical vs. Categorical
Continuous (Interval, Float, Numeric) Data
Discrete (Integer, Count) Data
Categorical (Enums, Enumerated, Factors, Nominal, Polychotomous) Data
Binary (Dichotomous, Logical, Indicator, Boolean) Data
Ordinal (Ordered Factor) Data
Data Storage and Retrieval
Vectors
Matrices
Arrays
Data Frames, Data Tables, and Tibbles
Lists
Factors
Useful Functions for Dataset Inspection and Manipulation
Other Data Types
Further Study
R Packages Required for This Chapter
References
3 Analysis of Accounting Transactions
Audit Procedures and Accounting Cycles
The Idiosyncratic Vocabulary of Audit Statistics
The Origin of Accounting Transactions
Audit Tests as Learning Models
Working with Dates
Accounting Transactions
Couching Institutional Language in Statistical Terms
Transaction Samples and Populations
Judgmental Sampling
Random Sampling
Fixed-Interval Sampling
Cell or Random-Interval Sampling
Random Sampling
Conditional Sampling
Stratified Sampling
Confidence Intervals
Materiality
Hypothesis Tests for a Material Error
Transaction or Record Sampling
Accounting Cycles
Substantive Testing
Metrics and Estimates
Machine Learning Methods
Statistical Perspectives on Audit Evidence and Its Information Content
Support and the Additivity of Evidence: The Log-Likelihood
The Score'' Fisher Information References 4 Risk Assessment and Planning Auditing The Financial Accounting Standards Board Risk Assessment in Planning the Audit Accessing the SEC's EDGAR Database of Financial Information Get Data Prepare Statements Validate Statement Calculation Hierarchy Merge Statements from Different Periods Calculate Financial Ratios Rearranging Statement Hierarchy Balance Sheet Visualization Prepare Custom Hierarchy Print as a Table Double Stacked Graph Audit Staffing and Budgets The Risk Assessment Matrix Using Shiny to Create a Risk Assessment Matrix Dashboard Generating the Audit Budget from the Risk Assessment Matrix Technical Sampling Structure of the Audit Program Sample Sizes for Budgeting Budget for Substantive Tests on Samples Notable Audit Failures and Why They Occurred Auditing: A Wicked Problem Final Thoughts on Audit Planning and Budgets References 5 Analytical Review: Technical Analysis Analytical Review Institutional Context of Analytical Review Technical Measures of a Company's Financial Health Purpose and Types of Ratios Common Technical Metrics Accessing Financial Information from EDGAR (https://www.sec.gov/edgar/) Accessing Financial Information from EDGAR (https://www.sec.gov/edgar/) with the finreportr Package Visualization of Technical Metrics Internet Resources for Analytical Review US Census Data R and Application Programming Interfaces (API) Technical Analysis of Product and Customer News Sources on the Web Vocabulary-Based Vectorization References 6 Analytical Review: Intelligence Scanning Intelligence Scanning of Internet Resources Sentiment Analysis with Tidy Data Scanning of Uncurated News Sources from Social Networks Intelligence Scanning of Curated News Streams Accessing General Web Content Through Web Scraping SelectorGadget Final Comments on Analytical Review with R 7 Design of Audit Programs Audit Programs as Natural Experiments Collecting and Analyzing Audit Evidence: Sampling AICPA Guidelines on Audit Sampling Sampling for Interim Tests of Compliance Discovery Sampling Attribute Sampling Acceptance Sampling Acceptance Sampling with Poisson Data A Statistical Decision Framework for Auditing Overview of Audit Statistical Testing Tasks Audit Tests as Learning Models Materiality Risk The AICPA on Sampling Approaches and Risks AICPA Pronouncements on Generally Accepted Auditing Standards Accounting Cycles Types of Sampling Allowed or Discussed by AICPA Judgmental Sampling Random Sampling Fixed-Interval Sampling Cell or Random-Interval Sampling Random Sampling Conditional Sampling Stratified Sampling Monetary Unit Sampling Confidence Intervals Materiality Hypothesis Tests for a Material Error Transaction or Record Sampling Accounting Transaction Distributions The Audit Cycle The Context of Auditing and Information Technology Auditors' Opinion: The Product of an Audit References 8 Interim Compliance Tests Interim Compliance Tests and the Management Letter The SAS 115 Letter to Management Three Methods of Sampling Discovery Sampling Discovery Sampling Attribute Sampling Attribute Sampling on Occurrences Attribute Sampling on Amounts Acceptance Sampling The Application of Sampling in Interim Tests Attribute Sampling with t-Tests Audit of Collection Transactions Machine Learning Models for Audit of Controls Autoencoders and Unbalanced Datasets Final Thoughts on Machine Learning Applications in Auditing References 9 Substantive Tests Substantive Tests Objective of Substantive Tests Exploratory Substantive Tests Creating Trial Balance Figures in One Step Accounts Receivable Auditing Footing and Agreeing to the Trial Balance Tests of Supporting Evidence Acceptance Sampling Accounts Receivable Confirmation Confirmations and Experimental Design Audit Program Procedures for Accounts Receivable Confirmation Timing of Confirmation Request Confirming Prior to Year End Steps in Confirmation Process Non-response to Confirmation Requests Confirmation Responses Not Expected Confirmation and Estimation of Error in Account Post-confirmation Tests Estimation and Accrual of the Allowance for Doubtful Accounts GLM-Exponential Regression Time Series Forecasting Forecasting Accounts Receivable Collections Calculating Allowance for Uncollectable Accounts Stratified Samples and Monetary Unit Sampling PPSTaintings'' and the Poisson–Poisson Compound Distribution
The Audit of Physical Inventory
Why Is a Physical Inventory Necessary?
Periodic Inventory Systems
Perpetual Inventory Systems
Counting Inventory When Preparing Financial Statements
Inventory Systems
Physical Inventory (Counting) Process
Physical Inventory Count Versus Cycle Counts
Inventory Audit Procedures
Cutoff Analysis
Test for Lower of Cost or Market (LOCOM)
References
10 Sarbanes–Oxley Engagements
The Sarbanes–Oxley Act: Security, Privacy, and Fraud Threats to Firm Systems
Academic Research on SOX Effectiveness
Evidence from Industry on SOX Effectiveness
Using R to Assess SOX Effectiveness in Predicting Breaches and Identifying Control Weaknesses
Exploratory Analysis of the SOX–Privacy Clearinghouse Dataset
Using an Autoencoder to Detect Control Weaknesses
Preprocessing
TensorFlow Implementation of the Autoencoder
The H2O Implementation of Autoencoders and Anomaly Detection for Fraud Analytics
Anomaly Detection
Pre-trained Supervised Model
Measuring Model Performance on Highly Unbalanced Data
Fama–French Risk Measures and Grid Search of Machine Learning Models to Augment Sarbanes–Oxley Information
Fama–French Risk Factors
Final Thoughts on Sarbanes–Oxley Reports
References
11 Blockchains, Large Language Models, Cybercrime, and Forensics
Blockchains for Securing Transactions
The Block'' Hashing Proof of Work (PoW) Adding Transactions (New Blocks) to the Blockchain Large Language Models Cybercrime and Forensics Forensic Analytics: Benford's Law References 12 Special Engagements: Forecasts and Valuation Special Engagements for Assurance and Valuation The Role of Valuations in the Market Hi-Tech and High Risk Strategy Drivers and Figures of Merit Valuation Models The Behavioral (Historical) Model Data: Transaction Stream Time Series Forecast Models Discount Model Terminal Dividend Generating a Current Valuation Other Approaches to Valuation Real Options Scenario Analysis and Decision Trees Monte Carlo Simulations Further Study References 13 Simulated Transactions for Auditing Service OrganizationsTest Decks'' and Their Progeny
Service Organization Audits
Accounting Cycles, Audit Tasks, and the Generation of Audit Data
Generation of Sales and Procurement Cycle Databases for A/B Testing of Audit Procedures
Setting Up the Simulation
Assumptions Used in the Generation of Simulated Data
Document-Updating Events in the Accounting Cycle All Have Identifier, Date, Number of Inventory Items, and Unit Value
Events Associated with Entities
Procurement Cycle (To Replace Sold Inventory; Amounts at Cost)
Reporting
Document Generation
Strategy for Document Generation
Statistical Assumptions and the Distribution of Accounting Transactions
General Parameters of the Simulation
The Sales Journal
Cash and Bank Deposits
Inventory and Purchase Orders for Inventory Replenishment
Inventory
Perpetual Inventory, Accounts Payable, and Other Inventory Related Accounts
Customer Credit Limits and Outstanding Accounts Receivable
Accounts Receivable
Accounts Receivable Aging
Employee Expenditures
Omissions, Duplications, and Monetary Errors in Transactions
Audit Tasks: Inventory and Accounts Receivable
Accounts Receivable Confirmations
Accounting Files for Audit
Auditing with Simulated Accounting Transactions
Index