AUDIT ANALYTICS AUDIT

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ESSAY 5 Data Analytics for Financial Statement Audits Trevor R. Stewart, CA, PhD ABSTRACT Data science and supporting technologies have advanced enormously in recent years, incorporating theories, techniques, and technologies from many fields, including mathematics and statistics; computer science; machine learning, including pattern recognition; data visualization; and data, text, and process mining. Data analytics (DA) has the potential to transform the way financial statement audits are conducted making them significantly more effective and possibly more efficient. There is an increasing recognition of this potential in the profession though few if any transformative applications have yet emerged, and there is a chronic shortage of data scientists and very few who understand auditing. There is an opportunity for firms, universities, professional bodies, standards setters, regulators, and solutions providers to collectively bring about transformative change. THE AUDIT CONTEXT DA as applied to financial statement auditing is the art and science of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling, and visualization for the purpose of planning or performing the audit. DA includes methodologies for identifying and analyzing anomalous patterns and outliers in data; 105
AUDIT ANALYTICS AND CONTINUOUS AUDIT:LOOKING TOWARD THE FUTURE mapping and visualizing financial performance and other data across operating units, systems, products, or other dimensions for the purpose of focusing the audit on risks; building statistical or other predictive models that explain the data in relation to other factors and identify significant fluctuations from the model; and combining information from disparate analyses and data sources for the purpose of gaining additional insights. DA and Generally Accepted Auditing Standards DA as just defined is and always has been integral to financial statement audits, pre-dating modern methods and technology. Indeed, it is difficult to imagine an auditor not using DA and still achieving the overall objectives set forth in auditing standards, which, to recap, are to a. obtain reasonable assurance about whether the financial statements as a whole are free from material misstatement, whether due to fraud or error, thereby enabling the auditor to express an opinion on whether the financial statements are presented fairly, in all material respects, in accordance with an applicable financial reporting framework; and b. report on the financial statements, and communicate as required by generally accepted auditing standards, in accordance with the auditor’s findings. Auditors have historically scanned financial statements and analyzed lists or summaries of transactions and balances; scrutinized journals for anomalous and unusual entries; compared results by month or operating unit; compared entities’ operations and results with industry peers and economic expectations; and considered events that might have affected the entity to ensure that they have been properly accounted for. It was ever thus, even in pen and ink days. Over the years, technology— whether it be Excel, ACL, IDEA, or the Internet—has improved the mechanics of how these tasks are performed, though the processes themselves have scarcely changed for decades. The emphasis in introducing technology to the audit process has been on improving both effectiveness and efficiency. While effectiveness has improved, there has not been the quantum leap that technology has the potential to enable. What is different now, in the second decade of the 21st century, is that extraordinary recent advances in fundamental data science, vast increases in computer power, and access to astronomical amounts of data and information have converged to provide an environment ripe for DA 106
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AUDIT ANALYTICS and CONTINUOUS AUDI
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Notice to Readers Audit Analytics a
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TABLE OF CONTENTS Preface Acknowled
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CONTENTS 3 Evolution of Auditing: F
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CONTENTS B Page Implementing Contin
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AUDIT ANALYTICS AND CONTINUOUS AUDI
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AUTHOR BIOGRAPHIES Abdullah Alawadh
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AUTHOR BIOGRAPHIES He is a passiona
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AUTHOR BIOGRAPHIES AICPA Assurance
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AUTHOR BIOGRAPHIES management. Prio
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AUTHOR BIOGRAPHIES Trevor R. Stewar
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PART I Essays 1
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CASE STUDY B Implementing Continuou
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CASE STUDY B: IMPLEMENTING CONTINUO
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CASE STUDY D Implementing Continuou
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CASE STUDY D: IMPLEMENTING CONTINUO
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AICPA Assurance Services Executive
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AUDIT ANALYTICS AUDIT

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