Audit Analytics Software
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Audit Analytics
Author: J. Christopher Westland
language: en
Publisher: Springer Nature
Release Date: 2024-04-04
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.
Audit Analytics in the Financial Industry
Split into six parts, contributors explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry. Chapters include topics such as fraud risks in the credit card sector, clustering techniques, fraud and anomaly detection, and using Audit Analytics to assess risk in the lawsuit and payment processes.
The Digital Transformation of Auditing and the Evolution of the Internal Audit
The main objective of this book is to provide both academics and practitioners with a global vision of the evolution of internal auditing in a fast-changing business landscape driven by digital transformation. Digital transformation has been first associated with the emergence and the development of new technologies (artificial intelligence, blockchain, cloud computing, data analytics, predictive analytics, robotic process automation, IOT, drones etc.). Beyond the technological dimensions, this transformation has several impacts on businesses, organizations and processes and raises several questions for auditing activities. This book explores how digitalization not only has an impact on the audit environment, but also on internal audit practices and methodologies, information technology (IT)/information system (IS) audit, IT governance and risk management. The auditing profession also has to face the same challenges. Auditors should develop new skills. To continue to provide high quality service in such an environment, the methodologies, the process and the tools used for conducting an audit have progressively changed from those applied to the traditional audit. Internal audit, as a key strategic function, must evolve too. Finally, the book also investigates the impact of the COVID-19 pandemic on internal auditing. The author highlights the need for a new vision and renewed forecasting tools. The post-COVID-19 business and corporate world has changed. Internal audit, as a key strategic function, must evolve too.