Financial process mining - Accounting data structure dependent control flow inference

Michael Werner

doi:10.1016/j.accinf.2017.03.004

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Financial process mining - Accounting data structure dependent control flow inference

2017-05-01

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Michael Werner *

DOI：10.1016/j.accinf.2017.03.004

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Abstract

En 中文

The increasing integration of computer technology for the processing of business transactions and the growing amount of financially relevant data in organizations create new challenges for external auditors. The availability of digital data opens up new opportunities for innovative audit procedures. Process mining can be used as a novel data analysis technique to support auditors in this context. Process mining algorithms produce process models by analyzing recorded event logs. Contemporary general purpose mining algorithms commonly use the temporal order of recorded events for determining the control flow in mined process models. The presented research shows how data dependencies related to the accounting structure of recorded events can be used as an alternative to the temporal order of events for discovering the control flow. The generated models provide accurate information on the control flow from an accounting perspective and show a lower complexity compared to those generated using timestamp dependencies. The presented research follows a design science research approach and uses three different real world data sets for evaluation purposes.

Keywords:

Process mining

Financial audits

Journal entries

Business process intelligence

Business process modeling

Control flow inference

Design science research

Enterprise resource planning systems

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