Data visualization for fraud detection: Practice implications and a call for future research

William N. Dilla and Robyn L. Raschke

International Journal of Accounting Information Systems, 2015, vol. 16, issue C, 1-22

Abstract: Analysis of data to detect transaction anomalies is an important fraud detection procedure. Interactive data visualization tools that allow the investigator to change the representation of data from text to graphics and filter out subsets of transactions for further investigation have substantial potential for making the detection of fraudulent transactions more efficient and effective. However, little research to date has directly examined the efficacy of data visualization techniques for fraud detection. In this paper, we develop a theoretical framework to predict when and how investigators might use data visualization techniques to detect fraudulent transactions. We use this framework to develop testable propositions and research questions related to this topic. The paper concludes by discussing how academic research might proceed in investigating the efficacy of interactive data visualization tools for fraud detection.

Keywords: Fraud detection; Visual analytics; Interactive data visualization; Decision aids; Cognitive fit theory (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1467089515000020
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ijoais:v:16:y:2015:i:c:p:1-22

DOI: 10.1016/j.accinf.2015.01.001

Access Statistics for this article

International Journal of Accounting Information Systems is currently edited by S.V. Grabski

More articles in International Journal of Accounting Information Systems from Elsevier
Bibliographic data for series maintained by Catherine Liu ().