Automated Credit Card Risk Assessment using Fuzzy Parameterized Neutrosophic Hypersoft Expert Set

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 ACCR