Articlebronze2018

Embracing Textual Data Analytics in Auditing with Deep Learning

94 citations0/0 files storedThe International Journal of Digital Accounting Research

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Computational and Text Analysis Methods Auditing, Earnings Management, Governance Robotic Process Automation Applications

Abstract

While the massive volume of text documents from multiple sources inside and outside of the company provides more information for auditors, the lack of efficient and effective technology solutions hampers the full use of text data.Powered by the emerging data analytics technology of deep learning, the value of the text can be better explored to deliver a higher quality of audit evidence and more relevant business insights.This research analyzes the usefulness of the information provided by various textual data in auditing and introduces deep learning, an evolving Artificial Intelligence approach.Furthermore, it provides a guide for auditors to implement deep learning techniques with predeveloped tools and open-source libraries.

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References

2011When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks 2007Giving Content to Investor Sentiment: The Role of Media in the Stock Market 2014Measuring Readability in Financial Disclosures 2007Determinants of weaknesses in internal control over financial reporting 2012Sentiment During Recessions 2009The Effect of Disclosures by Management, Analysts, and Business Press on Cost of Capital, Return Volatility, and Analyst Forecasts: A Study Using Content Analysis 2006The discovery and reporting of internal control deficiencies prior to SOX-mandated audits 2013Word power: A new approach for content analysis

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