Volume 4, Issue 1, PP: 36-40, 2024 | Cite this article as | XML | | Html PDF
Rhada Boujlil 1 * , Saad Alsunbul 2
The aim of this research is to examine the convergence of intelligent frameworks and financial fraud detection as a strategic approach for strengthening business sustainability in the banking industry. A rigorous preprocessing regimen, which includes data cleansing, normalization, and SMOTE algorithm application for class rebalancing, sets the stage for a refined dataset. Our proposed framework employs Logistic Regression, Decision Trees, and Gradient Boosting models to conduct a multifaceted analysis that accommodates both linear and non-linear relationships within the data. The results are presented through visual representations such as distribution plots and RoC curves that confirm the effectiveness of the framework in detecting potentially fraudulent activities. The comparative analysis offers detailed insights into how versatile the framework is. This study contributes to the broader discourse on intelligent systems in financial fraud detection with practical implications for businesses seeking to enhance their sustainability through advanced risk management strategies.
Business sustainability , financial fraud detection , corporate sustainability , Intelligent systems Fraud prevention , Economic resilience , Ethical finance.
[1] Chen, Hsinchun, Roger H L Chiang, and Veda C Storey. 2012. “Business Intelligence and Analytics: From Big Data to Big Impact.” MIS Quarterly, 1165–88.
[2] Ngai, Eric W T, Yong Hu, Yiu Hing Wong, Yijun Chen, and Xin Sun. 2011. “The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and an Academic Review of Literature.” Decision Support Systems 50 (3): 559–69.
[3] Mohamed, M. (2023) “Agricultural Sustainability in the Age of Deep Learning: Current Trends, Challenges, and Future Trajectories”, Sustainable Machine Intelligence Journal, 4, p. (2):1−20. doi: 10.61185/SMIJ.2023.44102.
[4] Abbasi, Ahmed, Conan Albrecht, Anthony Vance, and James Hansen. 2012. “Metafraud: A Meta-Learning Framework for Detecting Financial Fraud.” Mis Quarterly, 1293–1327.
[5] Zhu, Xiaoqian, Xiang Ao, Zidi Qin, Yanpeng Chang, Yang Liu, Qing He, and Jianping Li. 2021. “Intelligent Financial Fraud Detection Practices in Post-Pandemic Era.” The Innovation 2 (4).
[6] Al-Hashedi, Khaled Gubran, and Pritheega Magalingam. 2021. “Financial Fraud Detection Applying Data Mining Techniques: A Comprehensive Review from 2009 to 2019.” Computer Science Review 40: 100402.
[7] Buallay, Amina, and Jasim Al-Ajmi. 2020. “The Role of Audit Committee Attributes in Corporate Sustainability Reporting: Evidence from Banks in the Gulf Cooperation Council.” Journal of Applied Accounting Research 21 (2): 249–64.
[8] Casonato, Federica, Federica Farneti, and John Dumay. 2019. “Social Capital and Integrated Reporting: Losing Legitimacy When Reporting Talk Is Not Supported by Actions.” Journal of Intellectual Capital 20 (1): 144–64.
[9] Alnoukari, Mouhib, and Abdellatif Hanano. 2017. “Integration of Business Intelligence with Corporate Strategic Management.” Journal of Intelligence Studies in Business 7 (2).
[10] Windsor, Duane. 2006. “Corporate Social Responsibility: Three Key Approaches.” Journal of Management Studies 43 (1): 93–114.
[11] Goodell, John W, Satish Kumar, Weng Marc Lim, and Debidutta Pattnaik. 2021. “Artificial Intelligence and Machine Learning in Finance: Identifying Foundations, Themes, and Research Clusters from Bibliometric Analysis.” Journal of Behavioral and Experimental Finance 32: 100577.
[12] Chen, Jingqiu, Thomas Li-Ping Tang, and Ningyu Tang. 2014. “Temptation, Monetary Intelligence (Love of Money), and Environmental Context on Unethical Intentions and Cheating.” Journal of Business Ethics 123: 197–219.
[13] Kumar, V, Ashutosh Dixit, Rajshekar G Javalgi, and Mayukh Dass. 2016. “Research Framework, Strategies, and Applications of Intelligent Agent Technologies (IATs) in Marketing.” Journal of the Academy of Marketing Science 44: 24–45.
[14] Chung, Wingyan. 2014. “BizPro: Extracting and Categorizing Business Intelligence Factors from Textual News Articles.” International Journal of Information Management 34 (2): 272–84.
[15] Parker, Lee D. 2007. “Financial and External Reporting Research: The Broadening Corporate Governance Challenge.” Accounting and Business Research 37 (1): 39–54.
[16] Muthuswamy, M. and M. Ali, A. (2023) “Sustainable Supply Chain Management in the Age of Machine Intelligence: Addressing Challenges, Capitalizing on Opportunities, and Shaping the Future Landscape”, Sustainable Machine Intelligence Journal, 3. doi: 10.61185/SMIJ.2023.33103.
[17] Amani, Farzaneh A, and Adam M Fadlalla. 2017. “Data Mining Applications in Accounting: A Review of the Literature and Organizing Framework.” International Journal of Accounting Information Systems 24: 32–58.
[18] Sharma, Anuj, and Prabin Kumar Panigrahi. 2013. “A Review of Financial Accounting Fraud Detection Based on Data Mining Techniques.” ArXiv Preprint ArXiv:1309.3944.
[19] Trieu, Van-Hau. 2017. “Getting Value from Business Intelligence Systems: A Review and Research Agenda.” Decision Support Systems 93: 111–24.
[20] Abou Jaoude, Joe, and Raafat George Saade. 2019. “Blockchain Applications--Usage in Different Domains.” Ieee Access 7: 45360–81.
