Online Fraudulence Detection Based on Decision Support System in Digital Banking


  • Madasamy Raja. G Professor, Department of Information Technology, Paavai Engineering College, Namakkal, Tamilnadu
  • Amudha. G Professor, Computer Science and Business Systems, R.M.D. Engineering College, Chennai
  • G. Gomathy HOD, EEE Department, Jaya Engineering College, Chennai-24, Thiruvallur District, Tamil Nadu
  • P. Kowsalya Assistant Professor, Department of ECE, Madanapalle Institute of Technology & Science, Madanapalle, A.P
  • R. Salini Assistant professor, Department of CSE, Panimalar Engineering College, Chennai


DSS, online fraud detection, safe transactions, Banksealer architecture


The widespread use of online banking operations is anticipated to climb additionally as applications for digital banking progress. An unforeseen impact of this pattern is a surge in fraud attempts. On the other hand, the scientific research on spotting online banking scams is astonishingly thin. Our proposed solution is an attention-based structure that can be utilized to differentiate between truthful and bogus online banking transactions. In this article, a Decision Support System based on Machine Learning is proposed that automatically allocates a risk factor to each payment produced via a mobile device or online financial system. Since there is an enormous rise in the total amount of people using the internet, this suggested approach will be more effective in hindering unidentified hazards and malicious activity. The framework of the claimed method is structured: In advance of supplying a risk factor for activities that were not flagged as irregularities in the preceding phase, a controlled machine learning section is executed to recognize unusual behaviors or purchases that were mistakenly labeled. The final results of the simulation reveal that the concept of intelligent decision-making demonstrated in this paper has some real-world applications.


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How to Cite

Raja. G, M. ., G, A. ., Gomathy, G. ., Kowsalya, P. ., & Salini, R. . (2024). Online Fraudulence Detection Based on Decision Support System in Digital Banking. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 97–105. Retrieved from



Research Article