Audit Analytics: Data Science for the Accounting Profession
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| Format: | Electronic eBook |
| Language: | English |
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Cham
Springer International Publishing AG
2020
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| Series: | Use R! Ser
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| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (354 Seiten) |
| ISBN: | 9783030490911 |
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| 505 | 8 | |a Intro -- Foreword by Erik Brynjolfsson -- Foreword by Erik Brynjolfsson -- Preface -- How to Use This Book -- Contents -- Fundamentals of Auditing Financial Reports -- Auditing -- Computers in Auditing and the Birth of Audit Analytics -- The Roots of Modern Financial Accounting and Auditing -- Al-Khwarizmi Algebra of Double-Entry -- The Renaissance -- The Industrial Revolution -- The Birth of Modern Auditing -- Public Accounting -- Emerging Technologies and Intangible Assets -- Financial Accounting -- The Products of Accounting: Financial Statements -- The Balance Sheet -- The Income Statement -- Cash Flow Statements -- The Methodology of Accounting -- Generally Accepted Accounting Principles (GAAP) -- Theory -- Assumptions -- Principles -- Constraints -- The Accounting Process and Major Document Files -- Accounting Entries and Document Files -- Books of Accounts -- Code and Data Repositories for Audit Analytics -- R Packages Required for This Book -- References -- Foundations of Audit Analytics -- Business and Data Analytics -- Accounting Data Types -- Numerical vs. Categorical -- Categorical (Enums, Enumerated, Factors, Nominal, Polychotomous) Data -- Binary (Dichotomous, Logical, Indicator, Boolean) Data -- Ordinal (Ordered Factor) Data -- Data Storage and Retrieval -- Further Study -- R Packages Required for This Chapter -- References -- Analysis of Accounting Transactions -- Audit Procedures and Accounting Cycles -- 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 -- Accounting Cycles -- Substantive Testing -- Metrics and Estimates -- Important Concepts in Probability and Statistics -- Machine Learning Methods | |
| 505 | 8 | |a Statistical Perspectives on Audit Evidence and its Information Content -- Support and the Additivity of Evidence: The Log-Likelihood -- The ''Score'' -- Fisher Information -- Reference -- Risk Assessment and Planning -- Auditing -- Risk Assessment in Planning the Audit -- Accessing the SEC's EDGAR Database of Financial Information -- Caveats on accessing EDGAR information with R -- 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 -- Notable Audit Failures and Why They Occurred -- Auditing: A Wicked Problem -- Final Thoughts on Audit Planning and Budgets -- References -- 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 -- Computing Technical Metrics -- 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 -- Analytical Review: Intelligence Scanning -- Intelligence Scanning of Internet Resources -- Sentiment Analysis with Tidy Data -- Scanning of Uncurated News Sources from Social Networks -- Example: Extracting Tweets About General Motors and the Auto Industry -- Example: Extracting Tweets About Roland Musical Instruments and Their Industry -- Intelligence Scanning of Curated News Streams | |
| 505 | 8 | |a Accessing General Web Content through Web Scraping -- Final Comments on Analytical Review with R -- 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 -- Coefficient of Variation Formulas for Sample Size -- Rules of Threes to Calculate 95% Upper Confidence Bounds -- Attribute Sampling -- Acceptance Sampling -- Acceptance Sampling with Poisson Data -- A Statistical Decision Framework for Auditing -- Audit Tests as Learning Models -- Materiality -- Risk -- The AICPA on Sampling Approaches and Risks -- AICPA Pronouncements on Generally Accepted Auditing Standards -- Types of Sampling Allowed or Discussed by AICPA -- Judgmental Sampling -- Fixed-Interval Sampling -- Cell or Random-Interval Sampling -- Random Sampling -- Conditional Sampling -- Stratified Sampling -- Monetary Unit Sampling -- Transaction or Record Sampling -- Non-statistical Sampling -- Accounting Transaction Distributions -- The Audit Cycle -- The Context of Auditing and Information Technology -- Auditors' Opinion: The Product of an Audit -- References -- Interim Compliance Tests -- Interim Compliance Tests and the Management Letter -- The SAS 115 Letter to Management -- Three Methods of Sampling -- Discovery Sampling -- Attribute Sampling -- Statistics for 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 -- Substantive Tests -- Substantive Tests -- Objective of Substantive Tests -- Exploratory Substantive Tests -- Creating Trial Balance Figures in One Step -- Accounts Receivable Auditing | |
| 505 | 8 | |a Footing and Agreeing to the Trial Balance -- Tests of Supporting Evidence -- Acceptance Sampling -- Accounts Receivable Confirmation -- The Role of Confirmations as Audit Evidence -- 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 -- Probability Proportional to Size (PPS) Sampling -- Estimation and Accrual of the Allowance for Doubtful Accounts -- GLM-Exponential Regression -- Time-Series Forecasting -- Forecasting Accounts Receivable Collections -- Calculating Allowance for Uncollectable Accounts -- Idiosyncratic Tests -- Stratified Samples and Monetary Unit Sampling -- PPS -- Stringer's Perspective on Monetary Unit Sampling -- ''Taintings'' and the Poisson-Poisson Compound Distribution -- The Audit of Physical Inventory -- 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 -- Computer Analytic Workpaper Support -- Footing, Reconcile the Inventory Count to the General Ledger -- Cutoff Analysis -- Duplicates and Omissions -- Physical Count Exceptions to Perpetual Inventory Ledger -- Test for Lower of Cost or Market (LOCOM) -- Audit a Subset of High-Value Items -- Inventory Allowances -- Other Inventory Tests -- References -- 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 | |
| 505 | 8 | |a Using R to Assess SOX Effectiveness in Predicting Breaches, and Identifying Control Weaknesses -- Exploratory Analysis of the SOX-Privacy Clearninghouse 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 -- Blockchains, Cybercrime, and Forensics -- Blockchains for Securing Transactions -- The ''Block'' -- Hashing -- Proof-of-Work (PoW) -- Adding Transactions (New Blocks) to the Blockchain -- Cybercrime and Forensics -- Forensic Analytics: Benford's Law -- References -- Special Engagements: Forecasts and Valuation -- Special Engagements for Assurance and Valuation -- The Role of Valuations in the Market -- Hi-Tech, High Risk -- Strategy Drivers and Figures of Merit -- Corporate Figures of Merit -- The ROI Figure of Merit -- The Profit Figure of Merit -- The Sales Revenue Figure of Merit -- Opportunity Costs -- Valuation Models -- The Behavioral (Historical) Model -- Data: Transaction Stream Time Series -- How Much Information Is in a Dataset? -- 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 -- Simulated Transactions for Auditing Service Organizations -- ''Test Decks'' and Their Progeny -- Service Organization Audits -- Accounting Cycles, Audit Tasks, and the Generation of Audit Data | |
| 505 | 8 | |a Generation of Sales and Procurement Cycle Databases for A/B Testing of Audit Procedures | |
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| contents | Intro -- Foreword by Erik Brynjolfsson -- Foreword by Erik Brynjolfsson -- Preface -- How to Use This Book -- Contents -- Fundamentals of Auditing Financial Reports -- Auditing -- Computers in Auditing and the Birth of Audit Analytics -- The Roots of Modern Financial Accounting and Auditing -- Al-Khwarizmi Algebra of Double-Entry -- The Renaissance -- The Industrial Revolution -- The Birth of Modern Auditing -- Public Accounting -- Emerging Technologies and Intangible Assets -- Financial Accounting -- The Products of Accounting: Financial Statements -- The Balance Sheet -- The Income Statement -- Cash Flow Statements -- The Methodology of Accounting -- Generally Accepted Accounting Principles (GAAP) -- Theory -- Assumptions -- Principles -- Constraints -- The Accounting Process and Major Document Files -- Accounting Entries and Document Files -- Books of Accounts -- Code and Data Repositories for Audit Analytics -- R Packages Required for This Book -- References -- Foundations of Audit Analytics -- Business and Data Analytics -- Accounting Data Types -- Numerical vs. Categorical -- Categorical (Enums, Enumerated, Factors, Nominal, Polychotomous) Data -- Binary (Dichotomous, Logical, Indicator, Boolean) Data -- Ordinal (Ordered Factor) Data -- Data Storage and Retrieval -- Further Study -- R Packages Required for This Chapter -- References -- Analysis of Accounting Transactions -- Audit Procedures and Accounting Cycles -- 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 -- Accounting Cycles -- Substantive Testing -- Metrics and Estimates -- Important Concepts in Probability and Statistics -- 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 -- Reference -- Risk Assessment and Planning -- Auditing -- Risk Assessment in Planning the Audit -- Accessing the SEC's EDGAR Database of Financial Information -- Caveats on accessing EDGAR information with R -- 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 -- Notable Audit Failures and Why They Occurred -- Auditing: A Wicked Problem -- Final Thoughts on Audit Planning and Budgets -- References -- 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 -- Computing Technical Metrics -- 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 -- Analytical Review: Intelligence Scanning -- Intelligence Scanning of Internet Resources -- Sentiment Analysis with Tidy Data -- Scanning of Uncurated News Sources from Social Networks -- Example: Extracting Tweets About General Motors and the Auto Industry -- Example: Extracting Tweets About Roland Musical Instruments and Their Industry -- Intelligence Scanning of Curated News Streams Accessing General Web Content through Web Scraping -- Final Comments on Analytical Review with R -- 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 -- Coefficient of Variation Formulas for Sample Size -- Rules of Threes to Calculate 95% Upper Confidence Bounds -- Attribute Sampling -- Acceptance Sampling -- Acceptance Sampling with Poisson Data -- A Statistical Decision Framework for Auditing -- Audit Tests as Learning Models -- Materiality -- Risk -- The AICPA on Sampling Approaches and Risks -- AICPA Pronouncements on Generally Accepted Auditing Standards -- Types of Sampling Allowed or Discussed by AICPA -- Judgmental Sampling -- Fixed-Interval Sampling -- Cell or Random-Interval Sampling -- Random Sampling -- Conditional Sampling -- Stratified Sampling -- Monetary Unit Sampling -- Transaction or Record Sampling -- Non-statistical Sampling -- Accounting Transaction Distributions -- The Audit Cycle -- The Context of Auditing and Information Technology -- Auditors' Opinion: The Product of an Audit -- References -- Interim Compliance Tests -- Interim Compliance Tests and the Management Letter -- The SAS 115 Letter to Management -- Three Methods of Sampling -- Discovery Sampling -- Attribute Sampling -- Statistics for 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 -- 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 -- The Role of Confirmations as Audit Evidence -- 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 -- Probability Proportional to Size (PPS) Sampling -- Estimation and Accrual of the Allowance for Doubtful Accounts -- GLM-Exponential Regression -- Time-Series Forecasting -- Forecasting Accounts Receivable Collections -- Calculating Allowance for Uncollectable Accounts -- Idiosyncratic Tests -- Stratified Samples and Monetary Unit Sampling -- PPS -- Stringer's Perspective on Monetary Unit Sampling -- ''Taintings'' and the Poisson-Poisson Compound Distribution -- The Audit of Physical Inventory -- 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 -- Computer Analytic Workpaper Support -- Footing, Reconcile the Inventory Count to the General Ledger -- Cutoff Analysis -- Duplicates and Omissions -- Physical Count Exceptions to Perpetual Inventory Ledger -- Test for Lower of Cost or Market (LOCOM) -- Audit a Subset of High-Value Items -- Inventory Allowances -- Other Inventory Tests -- References -- 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 Clearninghouse 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 -- Blockchains, Cybercrime, and Forensics -- Blockchains for Securing Transactions -- The ''Block'' -- Hashing -- Proof-of-Work (PoW) -- Adding Transactions (New Blocks) to the Blockchain -- Cybercrime and Forensics -- Forensic Analytics: Benford's Law -- References -- Special Engagements: Forecasts and Valuation -- Special Engagements for Assurance and Valuation -- The Role of Valuations in the Market -- Hi-Tech, High Risk -- Strategy Drivers and Figures of Merit -- Corporate Figures of Merit -- The ROI Figure of Merit -- The Profit Figure of Merit -- The Sales Revenue Figure of Merit -- Opportunity Costs -- Valuation Models -- The Behavioral (Historical) Model -- Data: Transaction Stream Time Series -- How Much Information Is in a Dataset? -- 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 -- 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 |
| ctrlnum | (ZDB-30-PQE)EBC6403725 (ZDB-30-PAD)EBC6403725 (ZDB-89-EBL)EBL6403725 (OCoLC)1224141523 (DE-599)BVBBV048224525 |
| dewey-full | 657.450285 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 657 - Accounting |
| dewey-raw | 657.450285 |
| dewey-search | 657.450285 |
| dewey-sort | 3657.450285 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Electronic eBook |
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Ser</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Intro -- Foreword by Erik Brynjolfsson -- Foreword by Erik Brynjolfsson -- Preface -- How to Use This Book -- Contents -- Fundamentals of Auditing Financial Reports -- Auditing -- Computers in Auditing and the Birth of Audit Analytics -- The Roots of Modern Financial Accounting and Auditing -- Al-Khwarizmi Algebra of Double-Entry -- The Renaissance -- The Industrial Revolution -- The Birth of Modern Auditing -- Public Accounting -- Emerging Technologies and Intangible Assets -- Financial Accounting -- The Products of Accounting: Financial Statements -- The Balance Sheet -- The Income Statement -- Cash Flow Statements -- The Methodology of Accounting -- Generally Accepted Accounting Principles (GAAP) -- Theory -- Assumptions -- Principles -- Constraints -- The 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Statistics -- Machine Learning Methods</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Statistical Perspectives on Audit Evidence and its Information Content -- Support and the Additivity of Evidence: The Log-Likelihood -- The ''Score'' -- Fisher Information -- Reference -- Risk Assessment and Planning -- Auditing -- Risk Assessment in Planning the Audit -- Accessing the SEC's EDGAR Database of Financial Information -- Caveats on accessing EDGAR information with R -- 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 -- Notable Audit Failures and Why They Occurred -- Auditing: A Wicked Problem -- Final Thoughts on Audit Planning and Budgets -- References -- 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 -- Computing Technical Metrics -- 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 -- Analytical Review: Intelligence Scanning -- Intelligence Scanning of Internet Resources -- Sentiment Analysis with Tidy Data -- Scanning of Uncurated News Sources from Social Networks -- Example: Extracting Tweets About General Motors and the Auto Industry -- Example: Extracting Tweets About Roland Musical Instruments and Their Industry -- Intelligence Scanning of Curated News Streams</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Accessing General Web Content through Web Scraping -- Final Comments on Analytical Review with R -- 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 -- Coefficient of Variation Formulas for Sample Size -- Rules of Threes to Calculate 95% Upper Confidence Bounds -- Attribute Sampling -- Acceptance Sampling -- Acceptance Sampling with Poisson Data -- A Statistical Decision Framework for Auditing -- Audit Tests as Learning Models -- Materiality -- Risk -- The AICPA on Sampling Approaches and Risks -- AICPA Pronouncements on Generally Accepted Auditing Standards -- Types of Sampling Allowed or Discussed by AICPA -- Judgmental Sampling -- Fixed-Interval Sampling -- Cell or Random-Interval Sampling -- Random Sampling -- Conditional Sampling -- Stratified Sampling -- Monetary Unit Sampling -- Transaction or Record Sampling -- Non-statistical Sampling -- Accounting Transaction Distributions -- The Audit Cycle -- The Context of Auditing and Information Technology -- Auditors' Opinion: The Product of an Audit -- References -- Interim Compliance Tests -- Interim Compliance Tests and the Management Letter -- The SAS 115 Letter to Management -- Three Methods of Sampling -- Discovery Sampling -- Attribute Sampling -- Statistics for 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 -- Substantive Tests -- Substantive Tests -- Objective of Substantive Tests -- Exploratory Substantive Tests -- Creating Trial Balance Figures in One Step -- Accounts Receivable Auditing</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Footing and Agreeing to the Trial Balance -- Tests of Supporting Evidence -- Acceptance Sampling -- Accounts Receivable Confirmation -- The Role of Confirmations as Audit Evidence -- 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 -- Probability Proportional to Size (PPS) Sampling -- Estimation and Accrual of the Allowance for Doubtful Accounts -- GLM-Exponential Regression -- Time-Series Forecasting -- Forecasting Accounts Receivable Collections -- Calculating Allowance for Uncollectable Accounts -- Idiosyncratic Tests -- Stratified Samples and Monetary Unit Sampling -- PPS -- Stringer's Perspective on Monetary Unit Sampling -- ''Taintings'' and the Poisson-Poisson Compound Distribution -- The Audit of Physical Inventory -- 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 -- Computer Analytic Workpaper Support -- Footing, Reconcile the Inventory Count to the General Ledger -- Cutoff Analysis -- Duplicates and Omissions -- Physical Count Exceptions to Perpetual Inventory Ledger -- Test for Lower of Cost or Market (LOCOM) -- Audit a Subset of High-Value Items -- Inventory Allowances -- Other Inventory Tests -- References -- 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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Using R to Assess SOX Effectiveness in Predicting Breaches, and Identifying Control Weaknesses -- Exploratory Analysis of the SOX-Privacy Clearninghouse 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 -- Blockchains, Cybercrime, and Forensics -- Blockchains for Securing Transactions -- The ''Block'' -- Hashing -- Proof-of-Work (PoW) -- Adding Transactions (New Blocks) to the Blockchain -- Cybercrime and Forensics -- Forensic Analytics: Benford's Law -- References -- Special Engagements: Forecasts and Valuation -- Special Engagements for Assurance and Valuation -- The Role of Valuations in the Market -- Hi-Tech, High Risk -- Strategy Drivers and Figures of Merit -- Corporate Figures of Merit -- The ROI Figure of Merit -- The Profit Figure of Merit -- The Sales Revenue Figure of Merit -- Opportunity Costs -- Valuation Models -- The Behavioral (Historical) Model -- Data: Transaction Stream Time Series -- How Much Information Is in a Dataset? -- 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 -- Simulated Transactions for Auditing Service Organizations -- ''Test Decks'' and Their Progeny -- Service Organization Audits -- Accounting Cycles, Audit Tasks, and the Generation of Audit Data</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Generation of Sales and Procurement Cycle Databases for A/B Testing of Audit 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| id | DE-604.BV048224525 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:39Z |
| indexdate | 2024-07-10T09:32:29Z |
| institution | BVB |
| isbn | 9783030490911 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033605258 |
| oclc_num | 1224141523 |
| open_access_boolean | |
| physical | 1 Online-Ressource (354 Seiten) |
| psigel | ZDB-30-PQE |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| series2 | Use R! Ser |
| spelling | Westland, J. Christopher Verfasser aut Audit Analytics Data Science for the Accounting Profession Cham Springer International Publishing AG 2020 ©2020 1 Online-Ressource (354 Seiten) txt rdacontent c rdamedia cr rdacarrier Use R! Ser Description based on publisher supplied metadata and other sources Intro -- Foreword by Erik Brynjolfsson -- Foreword by Erik Brynjolfsson -- Preface -- How to Use This Book -- Contents -- Fundamentals of Auditing Financial Reports -- Auditing -- Computers in Auditing and the Birth of Audit Analytics -- The Roots of Modern Financial Accounting and Auditing -- Al-Khwarizmi Algebra of Double-Entry -- The Renaissance -- The Industrial Revolution -- The Birth of Modern Auditing -- Public Accounting -- Emerging Technologies and Intangible Assets -- Financial Accounting -- The Products of Accounting: Financial Statements -- The Balance Sheet -- The Income Statement -- Cash Flow Statements -- The Methodology of Accounting -- Generally Accepted Accounting Principles (GAAP) -- Theory -- Assumptions -- Principles -- Constraints -- The Accounting Process and Major Document Files -- Accounting Entries and Document Files -- Books of Accounts -- Code and Data Repositories for Audit Analytics -- R Packages Required for This Book -- References -- Foundations of Audit Analytics -- Business and Data Analytics -- Accounting Data Types -- Numerical vs. Categorical -- Categorical (Enums, Enumerated, Factors, Nominal, Polychotomous) Data -- Binary (Dichotomous, Logical, Indicator, Boolean) Data -- Ordinal (Ordered Factor) Data -- Data Storage and Retrieval -- Further Study -- R Packages Required for This Chapter -- References -- Analysis of Accounting Transactions -- Audit Procedures and Accounting Cycles -- 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 -- Accounting Cycles -- Substantive Testing -- Metrics and Estimates -- Important Concepts in Probability and Statistics -- 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 -- Reference -- Risk Assessment and Planning -- Auditing -- Risk Assessment in Planning the Audit -- Accessing the SEC's EDGAR Database of Financial Information -- Caveats on accessing EDGAR information with R -- 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 -- Notable Audit Failures and Why They Occurred -- Auditing: A Wicked Problem -- Final Thoughts on Audit Planning and Budgets -- References -- 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 -- Computing Technical Metrics -- 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 -- Analytical Review: Intelligence Scanning -- Intelligence Scanning of Internet Resources -- Sentiment Analysis with Tidy Data -- Scanning of Uncurated News Sources from Social Networks -- Example: Extracting Tweets About General Motors and the Auto Industry -- Example: Extracting Tweets About Roland Musical Instruments and Their Industry -- Intelligence Scanning of Curated News Streams Accessing General Web Content through Web Scraping -- Final Comments on Analytical Review with R -- 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 -- Coefficient of Variation Formulas for Sample Size -- Rules of Threes to Calculate 95% Upper Confidence Bounds -- Attribute Sampling -- Acceptance Sampling -- Acceptance Sampling with Poisson Data -- A Statistical Decision Framework for Auditing -- Audit Tests as Learning Models -- Materiality -- Risk -- The AICPA on Sampling Approaches and Risks -- AICPA Pronouncements on Generally Accepted Auditing Standards -- Types of Sampling Allowed or Discussed by AICPA -- Judgmental Sampling -- Fixed-Interval Sampling -- Cell or Random-Interval Sampling -- Random Sampling -- Conditional Sampling -- Stratified Sampling -- Monetary Unit Sampling -- Transaction or Record Sampling -- Non-statistical Sampling -- Accounting Transaction Distributions -- The Audit Cycle -- The Context of Auditing and Information Technology -- Auditors' Opinion: The Product of an Audit -- References -- Interim Compliance Tests -- Interim Compliance Tests and the Management Letter -- The SAS 115 Letter to Management -- Three Methods of Sampling -- Discovery Sampling -- Attribute Sampling -- Statistics for 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 -- 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 -- The Role of Confirmations as Audit Evidence -- 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 -- Probability Proportional to Size (PPS) Sampling -- Estimation and Accrual of the Allowance for Doubtful Accounts -- GLM-Exponential Regression -- Time-Series Forecasting -- Forecasting Accounts Receivable Collections -- Calculating Allowance for Uncollectable Accounts -- Idiosyncratic Tests -- Stratified Samples and Monetary Unit Sampling -- PPS -- Stringer's Perspective on Monetary Unit Sampling -- ''Taintings'' and the Poisson-Poisson Compound Distribution -- The Audit of Physical Inventory -- 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 -- Computer Analytic Workpaper Support -- Footing, Reconcile the Inventory Count to the General Ledger -- Cutoff Analysis -- Duplicates and Omissions -- Physical Count Exceptions to Perpetual Inventory Ledger -- Test for Lower of Cost or Market (LOCOM) -- Audit a Subset of High-Value Items -- Inventory Allowances -- Other Inventory Tests -- References -- 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 Clearninghouse 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 -- Blockchains, Cybercrime, and Forensics -- Blockchains for Securing Transactions -- The ''Block'' -- Hashing -- Proof-of-Work (PoW) -- Adding Transactions (New Blocks) to the Blockchain -- Cybercrime and Forensics -- Forensic Analytics: Benford's Law -- References -- Special Engagements: Forecasts and Valuation -- Special Engagements for Assurance and Valuation -- The Role of Valuations in the Market -- Hi-Tech, High Risk -- Strategy Drivers and Figures of Merit -- Corporate Figures of Merit -- The ROI Figure of Merit -- The Profit Figure of Merit -- The Sales Revenue Figure of Merit -- Opportunity Costs -- Valuation Models -- The Behavioral (Historical) Model -- Data: Transaction Stream Time Series -- How Much Information Is in a Dataset? -- 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 -- 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 Auditing-Data processing Accounting Bookkeeping Erscheint auch als Druck-Ausgabe Westland, J. Christopher Audit Analytics Cham : Springer International Publishing AG,c2020 9783030490904 |
| spellingShingle | Westland, J. Christopher Audit Analytics Data Science for the Accounting Profession Intro -- Foreword by Erik Brynjolfsson -- Foreword by Erik Brynjolfsson -- Preface -- How to Use This Book -- Contents -- Fundamentals of Auditing Financial Reports -- Auditing -- Computers in Auditing and the Birth of Audit Analytics -- The Roots of Modern Financial Accounting and Auditing -- Al-Khwarizmi Algebra of Double-Entry -- The Renaissance -- The Industrial Revolution -- The Birth of Modern Auditing -- Public Accounting -- Emerging Technologies and Intangible Assets -- Financial Accounting -- The Products of Accounting: Financial Statements -- The Balance Sheet -- The Income Statement -- Cash Flow Statements -- The Methodology of Accounting -- Generally Accepted Accounting Principles (GAAP) -- Theory -- Assumptions -- Principles -- Constraints -- The Accounting Process and Major Document Files -- Accounting Entries and Document Files -- Books of Accounts -- Code and Data Repositories for Audit Analytics -- R Packages Required for This Book -- References -- Foundations of Audit Analytics -- Business and Data Analytics -- Accounting Data Types -- Numerical vs. Categorical -- Categorical (Enums, Enumerated, Factors, Nominal, Polychotomous) Data -- Binary (Dichotomous, Logical, Indicator, Boolean) Data -- Ordinal (Ordered Factor) Data -- Data Storage and Retrieval -- Further Study -- R Packages Required for This Chapter -- References -- Analysis of Accounting Transactions -- Audit Procedures and Accounting Cycles -- 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 -- Accounting Cycles -- Substantive Testing -- Metrics and Estimates -- Important Concepts in Probability and Statistics -- 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 -- Reference -- Risk Assessment and Planning -- Auditing -- Risk Assessment in Planning the Audit -- Accessing the SEC's EDGAR Database of Financial Information -- Caveats on accessing EDGAR information with R -- 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 -- Notable Audit Failures and Why They Occurred -- Auditing: A Wicked Problem -- Final Thoughts on Audit Planning and Budgets -- References -- 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 -- Computing Technical Metrics -- 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 -- Analytical Review: Intelligence Scanning -- Intelligence Scanning of Internet Resources -- Sentiment Analysis with Tidy Data -- Scanning of Uncurated News Sources from Social Networks -- Example: Extracting Tweets About General Motors and the Auto Industry -- Example: Extracting Tweets About Roland Musical Instruments and Their Industry -- Intelligence Scanning of Curated News Streams Accessing General Web Content through Web Scraping -- Final Comments on Analytical Review with R -- 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 -- Coefficient of Variation Formulas for Sample Size -- Rules of Threes to Calculate 95% Upper Confidence Bounds -- Attribute Sampling -- Acceptance Sampling -- Acceptance Sampling with Poisson Data -- A Statistical Decision Framework for Auditing -- Audit Tests as Learning Models -- Materiality -- Risk -- The AICPA on Sampling Approaches and Risks -- AICPA Pronouncements on Generally Accepted Auditing Standards -- Types of Sampling Allowed or Discussed by AICPA -- Judgmental Sampling -- Fixed-Interval Sampling -- Cell or Random-Interval Sampling -- Random Sampling -- Conditional Sampling -- Stratified Sampling -- Monetary Unit Sampling -- Transaction or Record Sampling -- Non-statistical Sampling -- Accounting Transaction Distributions -- The Audit Cycle -- The Context of Auditing and Information Technology -- Auditors' Opinion: The Product of an Audit -- References -- Interim Compliance Tests -- Interim Compliance Tests and the Management Letter -- The SAS 115 Letter to Management -- Three Methods of Sampling -- Discovery Sampling -- Attribute Sampling -- Statistics for 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 -- 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 -- The Role of Confirmations as Audit Evidence -- 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 -- Probability Proportional to Size (PPS) Sampling -- Estimation and Accrual of the Allowance for Doubtful Accounts -- GLM-Exponential Regression -- Time-Series Forecasting -- Forecasting Accounts Receivable Collections -- Calculating Allowance for Uncollectable Accounts -- Idiosyncratic Tests -- Stratified Samples and Monetary Unit Sampling -- PPS -- Stringer's Perspective on Monetary Unit Sampling -- ''Taintings'' and the Poisson-Poisson Compound Distribution -- The Audit of Physical Inventory -- 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 -- Computer Analytic Workpaper Support -- Footing, Reconcile the Inventory Count to the General Ledger -- Cutoff Analysis -- Duplicates and Omissions -- Physical Count Exceptions to Perpetual Inventory Ledger -- Test for Lower of Cost or Market (LOCOM) -- Audit a Subset of High-Value Items -- Inventory Allowances -- Other Inventory Tests -- References -- 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 Clearninghouse 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 -- Blockchains, Cybercrime, and Forensics -- Blockchains for Securing Transactions -- The ''Block'' -- Hashing -- Proof-of-Work (PoW) -- Adding Transactions (New Blocks) to the Blockchain -- Cybercrime and Forensics -- Forensic Analytics: Benford's Law -- References -- Special Engagements: Forecasts and Valuation -- Special Engagements for Assurance and Valuation -- The Role of Valuations in the Market -- Hi-Tech, High Risk -- Strategy Drivers and Figures of Merit -- Corporate Figures of Merit -- The ROI Figure of Merit -- The Profit Figure of Merit -- The Sales Revenue Figure of Merit -- Opportunity Costs -- Valuation Models -- The Behavioral (Historical) Model -- Data: Transaction Stream Time Series -- How Much Information Is in a Dataset? -- 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 -- 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 Auditing-Data processing Accounting Bookkeeping |
| title | Audit Analytics Data Science for the Accounting Profession |
| title_auth | Audit Analytics Data Science for the Accounting Profession |
| title_exact_search | Audit Analytics Data Science for the Accounting Profession |
| title_exact_search_txtP | Audit Analytics Data Science for the Accounting Profession |
| title_full | Audit Analytics Data Science for the Accounting Profession |
| title_fullStr | Audit Analytics Data Science for the Accounting Profession |
| title_full_unstemmed | Audit Analytics Data Science for the Accounting Profession |
| title_short | Audit Analytics |
| title_sort | audit analytics data science for the accounting profession |
| title_sub | Data Science for the Accounting Profession |
| topic | Auditing-Data processing Accounting Bookkeeping |
| topic_facet | Auditing-Data processing Accounting Bookkeeping |
| work_keys_str_mv | AT westlandjchristopher auditanalyticsdatasciencefortheaccountingprofession |