Volume 25 , Issue 1 , PP: 93-103, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammed Abdullah Al-Hagery 1 , Abdalla I. Abdalla Musa 2
Doi: https://doi.org/10.54216/IJNS.250108
In the financial industry, financial fraud is an ever-evolving risk with extreme consequences. Data mining has been instrumental in the recognition of credit card fraud (CCF) during online transactions. CCF recognition, which is a data mining problem, become a challenge owing to its two main reasons - firstly, the profiles of fraudulent and normal behaviors modify continually and then, CCF dataset is extremely lopsided. The implementation of fraud recognition in credit card transactions is tremendously influenced by the sampling methodology on data, detection approach and variable selection utilized. The conception of the neutrosophic hypersoft set (NHSS) is a parameterized family that handles the sub-attributes of the parameter and is an appropriate extension of the NHSS to correctly evaluate the uncertainty, deficiencies, and anxiety in decision-making. In comparison to previous research, NHSS can accommodate additional uncertainty, which is the crucial approach to describe fuzzy datasets in the decision-making algorithm. This study introduces an Automated Credit Card Risk Assessment using Fuzzy Parameterized Neutrosophic Hypersoft Expert Set (ACCRA-FPNHES) technique. In the ACCRA-FPNHES technique, a three-step process is involved. As a primary step, the ACCRA-FPNHES technique designs sparrow search algorithm (SSA) for choosing features. In the second step, the detection of CCF takes place using FPNHES technique. Finally, in the third step, the parameters related to the FPNHES technique can be adjusted by arithmetic optimization algorithm (AOA). The simulation validation of the ACCRA-FPNHES technique can be studied on credit card dataset. The obtained values indicate that the ACCRA-FPNHES technique showcases better performance
Machine learning , Risk assessment , Artificial intelligence , Neutrosophic sets , Soft sets , Learning system
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