Multilevel Process Mining for Financial Audits

Michael Werner, Nick Gehrke

Research output: Contribution to journalJournal article

Abstract

The relevance of business intelligence increases with the growing amount of recorded data. The research on business intelligence has led to a mature set of methods and tools that are used in many application areas, but they are almost absent in the auditing industry. Public accountants face the challenge to audit increasingly complex business processes that process huge amounts of transaction data. Process mining can be used as a business intelligence approach in the context of process audits to exploit this data. We introduce a process mining algorithm to improve such audits. Key requirements for this purpose are the reliability of the mining results, the integration of a data flow perspective and the ability to inspect data from the point of origin to the final output on the financial accounts. The presented algorithm integrates the control flow and data flow perspective. It operates on different abstraction levels to enable the auditor to follow the audit trail. The algorithm creates precise and fitting process models to prevent false negative and false positive audit results, accepts specific unlabeled event logs as input, and considers data relationships for inferring the control flow. It was evaluated by using extensive real world data.
Original languageEnglish
Article number7277120
Journal IEEE Transactions on Services Computing
Volume8
Issue number6
Pages (from-to)820 - 832
Number of pages13
ISSN1939-1374
DOIs
Publication statusPublished - 2015
Externally publishedYes

Bibliographical note

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Keywords

  • Data mining
  • Competitive intelligence
  • Process control
  • Biological system modeling
  • Information systems
  • Data models

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