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Fraud Detection and Financial Analysis Based on Dombi Aggregation Operators for Neutrosophic Fuzzy Rough Information and their Applications
Zeeshan Ali, Hajra Bibi and Kostaq Hila
Fraud detection and financial analysis are two different and critical techniques within the realm of finance and business. Fraud detection is the procedure of using information analysis to simplify fraud activities and financial analysis is a procedure that assesses the financial health and performance of a business. The major influence of this manuscript is to evaluate the Dombi operational laws under the presence of the Neutrosophic fuzzy rough (NFR) information and simplify it with the help of suitable examples. Furthermore, we initiate the theory of NFR Dombi weighted averaging (NFRDWA) operator, NFR Dombi ordered weighted averaging (NFRDOWA) operator, NFR Dombi hybrid averaging (NFRDHA) operator, NFR Dombi weighted geometric (NFRDWG) operator, NFR Dombi ordered weighted geometric (NFRDOWG) operator, NFR Dombi hybrid geometric (NFRDHG) operator, and also derive their valuable properties. Additionally, we derive the technique of multi-attribute decision-making (MADM) problems for evaluating the problem of fraud detection and financial analysis under the consideration of the derived operators to enhance the worth of the initiated techniques. Finally, we compare theranking results of the proposed techniques and existing techniques to show the supremacy and validity of the initiated information.
Keywords: Dombi aggregation operators, neutrosophic fuzzy rough sets, fraud detection and financial analysis, decision-making problems