Volume 4 , Issue 1 , PP: 36-40, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
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.
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