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J. Christopher Westland - Audit Analytics_ Data Science for the Accounting Profession (Use R!) second editionDepartment of Information & Decision Sciences University of Illinois Chicago, IL, USA
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 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 PPS “Taintings” 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 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 Organizations . . . . . . . . . . . . . . . . “Test 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 |
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