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Visual Exploration of Journal Entries to Detect Accounting Irregularities and Fraud
Type
conference paper
Date Issued
2014-11-14
Author(s)
Abstract
Nowadays, companies and organizations register millions of accounting transactions each year. Although most of these journal entries are legitimate, auditors face legal and financial obligations to discover transactions that are fraudulent. In this work, we present a visual analytics workflow to quickly identify unusual transactions in accounting data. In a first step features are derived from journal entries and are clustered to identify transactional patterns. In a second step the data is visualized to support the identification and investigation of unusual transactions. Following this workflow auditors are given the chance to identify new suspicious transactions that might correspond to fraud. We evaluated the proposed approach in a real world scenario by analyzing accounting data and discussed the results with auditors.
Language
English
Keywords
Visual Analytics
Forensic Accounting
Financial Statement Fraud
Journal Entry Testing
HSG Classification
contribution to scientific community
Event Title
IEEE VIS 2014 Workshop "business | vis | 2014"
Event Location
Paris, France
Event Date
November 10, 2014
Official URL
Division(s)
Contact Email Address
marco.schreyer@unisg.ch
References
https://www.pwc.com/gx/en/about/stories-from-across-the-world/harnessing-the-power-of-ai-to-transform-the-detection-of-fraud-and-error.html
Additional Information
This work inspired PwC's innovation GL.ai which won the "Audit Innovation of Year" award of the International Accounting Bulletin in 2017.
Eprints ID
260689