2011
DOI: 10.2308/isys-10167
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Data Mining of Electronic Mail and Auditing: A Research Agenda

Abstract: A potentially important form of audit evidence is corporate email, which includes the background, intent, and outcome of managerial actions. Emails are semistructured data, with known fields for sender, recipient, subject, and date, as well as the email body and attachments. Data mining of emails (DME) combines understanding of social networks, textual analysis using natural language processing and other techniques, and domain knowledge. Email data mining research received a considerable boost by the availabil… Show more

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Cited by 14 publications
(3 citation statements)
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References 85 publications
(94 reference statements)
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“…Some e-mails specific to certain individuals were removed for privacy and legal reasons (Diesner et al , 2005). This corpus offers an opportunity for e-mail data mining (Debreceny and Gray, 2011) due to its conversational nature (Lindsey et al , 2007) and reflection of authentic reality. As with a variety of studies drawing on the Enron corpus (cf.…”
Section: Methodsmentioning
confidence: 99%
“…Some e-mails specific to certain individuals were removed for privacy and legal reasons (Diesner et al , 2005). This corpus offers an opportunity for e-mail data mining (Debreceny and Gray, 2011) due to its conversational nature (Lindsey et al , 2007) and reflection of authentic reality. As with a variety of studies drawing on the Enron corpus (cf.…”
Section: Methodsmentioning
confidence: 99%
“…Studies have also shown that the use of CAATs has different effect on internal and external auditors [21][22][23]. Factors influencing the use of CAATs include accomplishment of ISA statements and the supervision from the National Audit statutory bodies, training, etc.…”
Section: Standards Of Reportingmentioning
confidence: 99%
“…The application domains range from the healthcare and IT sectors to the textile, shipping and manufacturing industry. Scholars have also applied process mining for the purpose of internal auditing [25] and general data mining techniques on journal entry data for auditing [26] and internal fraud detection purposes [27].…”
Section: Related Literaturementioning
confidence: 99%