Automatic Card Fraud Detection Based on Decision Tree Algorithm

References

• Abiola and Idowu. 2009. An assessment of fraud and its management in Nigeria commercial banks. European Journal of Social Sciences 10 (4):628–26.
   Google Scholar
• Adetiloye, K. A., F. O. Olokoyo, and J. N. Taiwo. 2016. Fraud prevention and internal control in the Nigerian banking system. International Journal of Economics and Financial Issues 6:1172–79.
   Google Scholar
• Akers, M. D., and J. L. Gissel. 2006. What is fraud and who is responsible? Journal of Forensic Accounting 7 (1).
   Google Scholar
• Albrecht, W. S. 2003. Fraud examination. 3rd ed. South-Western College Pub.
   Google Scholar
• Al-Hashedi, K., and P. Magalingam. 2021. Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Computer Science Review 40:1–23. doi:10.1016/j.cosrev.2021.100402.
   Web of Science ®Google Scholar
• Aljohani, K., S. Aljuaid, and A. Aljarboa. 2021. Fraud prevention policies and strategies of banks in Saudi Arabia. Journal of Financial Crime 28 (1):200–15. doi:10.1108/JFC-09-2020-0151.
   Google Scholar
• Alraddadi, A. S. 2023. A survey and a credit card fraud detection and prevention model using the decision tree algorithm. Engineering, Technology & Applied Science Research 13 (4):11505–10. doi:10.48084/etasr.6128.
   Web of Science ®Google Scholar
• Altman, E. R. 2019. Synthesizing credit card transactions. CoRR. doi: 10.1145/3490354.3494378.
   Google Scholar
• Athey, S. 2019. The impact of machine learning on economics. In The economics of artificial intelligence: An agenda, ed. A. Agrawal, J. Gans, and A. Goldfarb, 507–47. University of Chicago Press. Accessed July 20th, 2023. http://www.nber.org/chapters/c14009.
   Google Scholar
• Bhasin, M. 2016. The role of technology in combating bank frauds: Perspectives and prospects. Ecoforum Journal 5 (2):200–212.
   Google Scholar
• Bhattacharyya, S., S. Jha, K. Tharakunnel, and J. Westland. 2011. Data mining for credit card fraud: A comparative study. Decision Support Systems 50 (3):602–13. doi:10.1016/j.dss.2010.08.008.
   Web of Science ®Google Scholar
• Chaudhary, K., J. Yadav, and B. Mallick. 2012. A review of fraud detection techniques: Credit card. International Journal of Computer Applications 45:39–44.
   Google Scholar
• Crowe, J. T. 2015. Fraud pentagon theory. Journal of Accountancy 220 (2):28–33.
   Google Scholar
• Dzomira, S. 2015. Fraud prevention and detection. Research Journal of Finance & Accounting 6:40–43.
   Google Scholar
• Enofe, A., T. Abilogun, A. Omoolorun, and E. Elaiho. 2017. International journal of academic research in business and social sciences. The International Journal of Academic Research in Business & Social Sciences 7 (7):110–21.
   Google Scholar
• Ernst and Young. 2009. Detecting financial statement fraud. Tech. rep. Ernst and Young.
   Google Scholar
• FICO. 2023. European fraud. Accessed April 9, 2023. https://www.fico.com/europeanfraud/.
   Google Scholar
• Gbegi, D. O., and A. J. F. 2013. The new fraud diamond model - how can it help forensic accountants in fraud investigation in Nigeria? European Journal of Accounting Auditing and Fiancé Research 1 (4):129–138.
   Google Scholar
• Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer.
   Google Scholar
• Hoffmann, A., and C. Birnbrich. 2012. The impact of fraud prevention on bank-customer relationships: An empirical investigation in retail banking. International Journal of Bank Marketing 30 (5):390–407. doi: 10.1108/02652321211247435.
   Google Scholar
• Kaggle. 2020. Credit card transactions fraud detection dataset. Accessed May 11, 2023. https://www.kaggle.com/datasets/kartik2112/fraud-detection.
   Google Scholar
• Kaggle. 2021. Credit card transactions. Accessed September 24, 2023. https://www.kaggle.com/datasets/ealtman2019/credit-card-tran-sactions.
   Google Scholar
• Khanna, A., and B. Arora. 2009. A study to investigate the reasons for bank fraud and the implementation of preventive security controls in the Indian banking industry. International Journal of Business Science & Applied Management 4:3.
   Google Scholar
• Kotsiantis, S. B., I. D. Zaharakis, and P. E. Pintelas. 2007. Supervised machine learning: A review of classification techniques. Informatica 31 (3):249–68.
   Google Scholar
• Machado, M. R. R., and I. R. Gartner. 2017. Cressey hypothesis (1953) and an investigation into the occurrence of corporate fraud: An empirical analysis conducted in Brazilian banking institutions. https://api.semanticscholar.org/CorpusID:59024427.
   Google Scholar
• Mahesh, B. 2020. Machine learning algorithms - a review. International Journal of Science and Research 9 (1):381–86. doi:10.21275/ART2020399.
   Google Scholar
• Manurung, D. T. H., and N. Hadian. 2017. Detection fraud of financial statement with fraud triangle. International Journal of Business, Economics and Law 12 (4):31–34.
   Google Scholar
• Mullainathan, S., and Z. Obermeyer. 2017. Does machine learning automate moral hazard and error? The American Economic Review 107 (5):476–80. doi:10.1257/aer.p20171084.
   PubMed Web of Science ®Google Scholar
• Nabhan, R., and N. Hindi. 2009. Bank fraud: Perception of bankers in the state of qatar. Academy of Banking Studies Journal 8:15–38.
   Google Scholar
• Nilson Report. 2023. Nilson report research publication. Accessed March 13, 2023.
   Google Scholar
• Nwankwo, O. F. 2013. Implications of fraud on commercial banks performance in nigeria. International Journal of Biometrics 8 (15):144. doi:10.5539/ijbm.v8n15p144.
   Google Scholar
• Olaleye Ayorinde, K. 2021. A methodology for detecting credit card Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University. Master’s thesis, Mankato.
   Google Scholar
• Omar, N. B., and H. F. Mohamad Din. 2010. Fraud diamond risk indicator: An assessment of its importance and usage. 2010 International Conference on Science and Social Research (CSSR 2010), 607–12. doi:10.1109/CSSR2010.5773853.
   Google Scholar
• Plakandaras, V., P. Gogas, T. Papadimitriou, and I. Tsamardinos. 2022. Credit card fraud detection with automated machine learning systems. Applied Artificial Intelligence 36 (1):2086354. doi:10.1080/08839514.2022.2086354.
   Web of Science ®Google Scholar
• Rahman, R. A., and I. S. K. Anwar. 2014. Effectiveness of fraud prevention and detection techniques in Malaysian Islamic banks. Procedia - Social & Behavioral Sciences 145:97–102. doi:10.1016/j.sbspro.2014.06.015.
   Google Scholar
• Repousis, S., P. Lois, and V. Veli. 2019. An investigation of the fraud risk and fraud scheme methods in Greek commercial banks. Journal of Money Laundering Control 22 (1):53–61. doi:10.1108/JMLC-11-2017-0065.
   Web of Science ®Google Scholar
• Ruankaew, T. 2013. The fraud factors. Accessed July 2023. https://oa.mg/work/2183440902.
   Google Scholar
• Save, P., P. Tiwarekar, K. N. Jain, and N. Mahyavanshi. 2017. A novel idea for credit card fraud detection using decision tree. International Journal of Computer Applications 161 (13):6–9. doi:10.5120/ijca2017913413.
   Google Scholar
• Scikit-learn: Machine learning in Python. n.d. https://scikit-learn.org/stable/.
   Google Scholar
• Tunji, S. 2013. Effective internal control system as an antidote for distress in the banking industry in nigeria. Journal of Economics and International Business Research 1 (5):106–21.
   Google Scholar
• Vonasek, J. 2013. Fraud detection in unlabeled payment card transactions. Bachelor’s Project, Czech Technical University in Prague, 1–63.
   Google Scholar
• West, J., and M. Bhattacharya. 2016. Intelligent financial fraud detection: A comprehensive review. Computers & Security 57:47–66. doi:10.1016/j.cose.2015.09.005.
   Web of Science ®Google Scholar
• Wolfe, D. T., and D. R. Hermanson. 2004. The fraud diamond: Considering the four elements of fraud. The CPA Journal 74 (12):38–42.
   Google Scholar
• Xuan, S., G. Liu, and Z. Li. 2018. Refined weighted random forest and its application to credit card fraud detection. Computational Data and Social Networks: 7th International Conference, CSoNet 2018, 343–55, Shanghai, China, December 18–20, 2018, Proceedings 7.
   Google Scholar
• Zhu, Z.-H. 2021. Machine learning. Singapore: Springer Nature. doi: 10.1007/978-987-15-1967-3.
   Google Scholar