Methodological contributions for the fightind fraud: A multidisciplinary challenge
DOI:
https://doi.org/10.3145/thinkepi.2018.60Keywords:
Financial fraud, Scientific techniques, Detection, Multidisciplinary, Fraud triangle.Abstract
State of the art scientific methods can be used against financial fraud, mainly of a technical nature (computer, economic, mathematical). Although some of the scientific areas involved are not related to technology, such as sociology, it is important that all disciplines provide useful tools to detect fraud. The detection and prevention of financial fraud is a multidisciplinary task, so the solution to this urgent problem needs to be provided by multidisciplinary teams.
References
Abdullahi, Rabi´u; Mansor, Noorhayati (2015). "Fraud triangle theory and fraud diamond theory. Understanding the convergent and divergent for future research". International journal of Academic Research in Accounting, Finance and Management Sciences, v. 5, n. 4, pp. 38-45. http://www.iiste.org/Journals/index.php/EJBM/article/viewFile/26274/26919
Dorminey, Jack; Fleming, A. Scott; Kranacher, Mary-Jo; Riley, Richard A. Jr. (2012). "The evolution of fraud theory". Issues in accounting education, v. 27, n. 2, pp. 555-579. https://doi.org/10.2308/iace-50131
Mock, Theodore; Srivastava, Rajendra; Wright, Arnold M. (2017). "Fraud risk assessment using the fraud risk model as a decision aid". Journal of emerging technologies in accounting, v. 14, n. 1, pp. 37-56. https://doi.org/10.2308/jeta-51724
Naruedomkul, Pornchai; Rodwanna, Pannipa; Wonglimpiyarat, Jarunee (2010). "Organization frauds in Thailand: A survey on risk factors". International journal of criminal justice sciences, v. 5, n. 1, pp. 203-219. https://goo.gl/Zb2rkq
Ngai, Eric W.T.; Hu, Yong; Wong, Y. H.; Chen, Yijun; Sun, Xin (2011). "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature". Decision support systems, v. 50, n. 3, pp. 559-569. https://doi.org/10.1016/j.dss.2010.08.006
Richhariya, Pankaj; Singh, Prashant (2012). "A survey on financial fraud detection methodologies". International journal of computer applications, v. 45, n. 22, pp. 975-1007.
Szárnyas, Gábor; Koovári, Zsolt; Salánki, Ágnes; Varró, Dániel (2016). "Towards the characterization of realistic models: Evaluation of multidisciplinary graph metrics". In: Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems, pp. 87-94. https://inf.mit.bme.hu/sites/default/files/publications/models2016-metrics.pdf
Trompeter, Gregory M.; Carpenter, Tina D.; Jones, Keith L.; Riley, Richard A. Jr. (2014). "Insights for research and practice: What we learn about fraud from other disciplines". Accounting horizons, v. 28, n. 4, pp. 769-804. https://doi.org/10.2308/acch-50816
West, Jarrod; Bhattacharya, Maumita (2016). "Intelligent financial fraud detection: a comprehensive review". Computers & security, v. 57, pp. 47-66. https://doi.org/10.1016/j.cose.2015.09.005
Wilks, T. Jeffrey; Zimbelman, Mark F. (2004). "Using game theory and strategic reasoning concepts to prevent and detect fraud". Accounting horizons, v. 18, n. 3, pp. 173-184. https://doi.org/10.2308/acch.2004.18.3.173
Zhao, Jie; Lau, Raymond Y.K.; Zhang, Wenping; Zhang, Kaihang; Chen, Xu; Tang, Deyu (2016). "Extracting and reasoning about implicit behavioral evidences for detecting fraudulent online transactions in e-Commerce". Decision support systems, v. 86, pp. 109-121. https://doi.org/10.1016/j.dss.2016.04.003
