Analysis of Performance Anomaly and Fraudster Profile for Fraud Prevention and Detection
DOI:
https://doi.org/10.21532/apfjournal.v8i2.309Keywords:
Fraudster Profile, Anomaly Data, Data AnalyticsAbstract
The rapid development of technology provides us with a lot of data that can be used for various purposes, such as fraud risk management. Data analytics should be the basis for anti-fraud activities related to prevention and detection processes. This study aims to elaborate on the data analytics used in developing fraud red flags based on historical reports. By applying anomaly data analytics and demographic profiles of fraudsters, this study finds that performance anomalies contribute 68% to fraud, while 3 to 10 years of service without career advancement can trigger motivation to commit fraud. Finally, the paper recommends that data analytics should be followed by human approaches such as lifestyle audits and career advancement programs. Further research is expected to be able to complement other parameters for data analysis and use statistical methods to obtain more accurate results.References
ACFE. (n.d.). https://www.acfe.com/fraud-resources/fraud-101-what-is-fraud.
Achmad, T., Ghozali, I., & Pamungkas, I. D. (2022). Hexagon Fraud: Detection of Fraudulent Financial Reporting in State-Owned Enterprises Indonesia. Economies, 10(1). 10.3390/economies10010013.
Anindya, J. R., & Adhariani, D. (2019). Fraud risk factors and tendency to commit fraud: analysis of employees’ perceptions. International Journal of Ethics and Systems, 35(4), 545–557. 10.1108/IJOES-03-2019-0057.
Association of Certified Fraud Examiners (ACFE). (2022). Occupational Fraud 2022: A Report to the Nations. https://acfepublic.s3.us-west-2.amazonaws.com/2022+Report+to+the+Nations.pdf
Bănărescu, A. (2015). Detecting and Preventing Fraud with Data Analytics. Procedia Economics and Finance, 32, 1827–1836. 10.1016/S2212-5671(15)01485-9.
CIMA. (2008). Fraud Risk Management: A Guide To Good Practice. Chartered Institute of Management Accountants
Dataiku. (2020). Fraud and Anomaly Detection in Banking A Step-by-Step Guide to Incorporating Machine Learning Into Models. www.dataiku.com
Deloitte. (2020). Covid 19 and Fraud Risk: Managing and responding in times of crisis.
Deloitte. (2021). Managing fraud risk: prevent, detect, and respond.
EY Building a Better Working World. (2020). COVID-19 Implications: Internal Fraud Minds Made For Protecting Financial Services. Ernst & Young Global Limited.
Ghazali, M. Z., Rahim, M. S., Ali, A., & Abidin, S. (2014). A Preliminary Study on Fraud Prevention and Detection at the State and Local Government Entities in Malaysia. Procedia-Social and Behavioral Sciences, 164, 437–444. 10.1016/J.SBSPRO.2014.11.100.
KPMG. (2011). Who is the typical fraudster?
Massa, D., & Valverde, R. (2014). A Fraud Detection System Based on Anomaly Intrusion Detection Systems for E-Commerce Applications. Computer and Information Science, 7(2). 10.5539/CIS.V7N2P117.
Maulidi, A. (2020). When and why (honest) people commit fraudulent behaviours?: Extending the fraud triangle as a predictor of fraudulent behaviours. Journal of Financial Crime, 27(2), 541–559. 10.1108/JFC-05-2019-0058.
Mustika, N. I., Nenda, B., & Ramadhan, D. (2021). Machine Learning Algorithms in Fraud Detection: Case Study on Retail Consumer Financing Company. Asia Pacific Fraud Journal, 6(2), 213–221. 10.21532/apfjournal.v6i2.216.
Ngosa, P. B., & Mwanza, J. (2021). Study of Profiling the Typical Fraudster in the General Education Sector in Zambia. International Journal of Advances in Scientific Research and Engineering, 7(8), 82–90. 10.31695/IJASRE.2021.34061.
OJK. (2018). POJK Nomor 35/POJK.05/2018 tentang Penyelenggaraan Usaha Perusahaan Pembiayaan. Otoritas Jasa Keuangan
POJK Nomor 44/POJK.05/2020 tentang Penerapan Manajemen Risiko Bagi Lembaga Jasa Keuangan Nonbank, (2020) (testimony of Otoritas Jasa Keuangan (OJK)).
Pinto, S. O., & Sobreiro, V. A. (2022). Literature Review: Anomaly De-tection Approaches on Digital Business Financial Systems. Digital Business, 2(2), 100038. 10.1016/J.DIGBUS.2022.100038.
Pourhabibi, T., Ong, K. L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, 113303. 10.1016/J.DSS.2020.113303.
PricewaterhouseCoopers (PWC). (2020). COVID-19 and the Indonesian Banking Industry: Issues and actions to consider. http://www.pwc.com/structure
Rahman, R. A., & Anwar, I. S. K. (2014). Effectiveness of Fraud Prevention and Detection Techniques in Malaysian Islamic Banks. Procedia-Social and Behavioral Sciences, 145, 97–102. 10.1016/J.SBSPRO.2014.06.015.
Ramadhan, D. (2020). Root Cause Analysis Using Fraud Pentagon Theory Approach (A Conceptual Framework). Asia Pacific Fraud Journal, 5(1), 118–125. 10.21532/apfjournal.v5i1.142.
Schwab, K. (2016). The Fourth Industrial Revolution. World Economic Forum. www.weforum.org.
Utami, I., Wijono, S., Noviyanti, S., & Mohamed, N. (2019). Fraud Diamond, Machiavellianism And Fraud Intention. International Journal of Ethics and Systems, 35(4), 531–544. 10.1108/IJOES-02-2019-0042.
Varma, T. N., & Khan, D. A. (2016). Greed an Attribute of Fraudster. AIMS International Journal of Management, 10(2), 83–99.
Yessi Puspitha, M., & Wirawan Yasa, G. (2018). Fraud Pentagon Analysis in Detecting Fraudulent Financial Reporting (Study on Indonesian Capital Market). International Journal of Sciences: Basic and Applied Research, 42(5), 93–109.
Yusti, M., Triyadi, T., & Ramadhan, D. (2021). Analysis of the Root Causes of Fraud Using Risk Causal and Fraud Diamond Matrix: A Case Study on Retail Financing Company. Asia Pacific Fraud Journal, 6(1), 159–170. 10.21532/apfjournal.v6i1.202.
