International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/2783 2020 2020 Department of Financial and Banking Sciences, Applied College at Muhail Aseer, King Khalid University, Saudi Arabia. Fadoua Fadoua Financial fraud may be regarded as any fraud targeting financial organisations including crypto exchanges, banks, fintech, and lending organizations, or any criminal activity associated with the payment process. Financial fraud detection cites protocol set prepared to circumvent the destruction produced by fraudulent activities happening in financial service suppliers. Ecological financial fraud detection (FD) includes the usage of ethical and sustainable performs within fraud actions recognition from the financial area. In recent times, DL and ML techniques have been used in CCF recognition owing to their ability to construct a robust mechanism to discover fraud businesses. Therefore, this study develops an Optimal Single Valued Neutrosophic Sine Trigonometric Aggregation Operator (O-SVNSTAO) for Accurate Financial Fraud Detection Model. The genetic-inspired particle swarm optimization (GIPSO) feature selection model efficiently discerns the relevant attribute from sophisticated financial databases, improving the model's discriminative power while alleviating dimensionality problems. Consequently, the SVNSTAO classifier leverages the features selected to discern complicated features inherent in fraudulent actions, which facilitates accurate diagnosis. Moreover, the COA parameter tuning mechanism enhances the SVNSTAO model's parameter, which ensures adaptability and optimum performance to varied fraud settings. Empirical analysis of real-time financial datasets demonstrates the superiority of O-SVNSTAO technique over classical methods, underlining its effectiveness in discovering financial fraud with exceptional efficiency and reliability 2024 2024 415 425 10.54216/IJNS.230434 https://www.americaspg.com/articleinfo/21/show/2783