Audit analytics: data science for the accounting profession

By:

Westland, Christopher J

Material type: Text

TextSeries: Use RPublication details: Springer Cham 2024Edition: 2ndDescription: xiv, 470 pISBN:

9783031474637

Subject(s):

DDC classification:

657.450285 WES

Summary: 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. (https://link.springer.com/book/10.1007/978-3-031-47464-4)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Finance & Accounting	657.450285 WES (Browse shelf(Opens below))	1	Available		007562

Table of contents:
Front Matter
Pages I-XIV
Download chapter PDF
Fundamentals of Auditing Financial Reports
J. Christopher Westland
Pages 1-22
Foundations of Audit Analytics
J. Christopher Westland
Pages 23-52
Analysis of Accounting Transactions
J. Christopher Westland
Pages 53-82
Risk Assessment and Planning
J. Christopher Westland
Pages 83-108
Analytical Review: Technical Analysis
J. Christopher Westland
Pages 109-141
Analytical Review: Intelligence Scanning
J. Christopher Westland
Pages 143-171
Design of Audit Programs
J. Christopher Westland
Pages 173-203
Interim Compliance Tests
J. Christopher Westland
Pages 205-254
Substantive Tests
J. Christopher Westland
Pages 255-336
Sarbanes–OxleyEngagements
J. Christopher Westland
Pages 337-382
Blockchains, Large Language Models, Cybercrime, and Forensics
J. Christopher Westland
Pages 383-402
Special Engagements: Forecasts and Valuation
J. Christopher Westland
Pages 403-420
Simulated Transactions for Auditing Service Organizations
J. Christopher Westland
Pages 421-460
Back Matter
Pages 461-470

[https://link.springer.com/book/10.1007/978-3-031-47464-4]

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.

(https://link.springer.com/book/10.1007/978-3-031-47464-4)

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