International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/3965 2020 2020 Faculty of Economics, RUDN University, Moscow, 117198, Russia; Khorezm University of Economics, Urgench, 220100, Uzbekistan Elvir Elvir Department of Business and Management, Urgench State University, Urgench, 220100, Uzbekistan Ilyos Abdullayev Department of Accounting and Business Management, Mamun University, Khiva, 220900, Uzbekistan; Department of Accounting and Auditing, Tashkent Institute of Irrigation and Agricultural Mechanisation Erkin Shodiev Department of Finance, Alfraganus University, Tashkent, 100000, Uzbekistan; Department of Finance and Tourism, Termez University of Economics and Service, Termez, 190111, Uzbekistan; Center of the Engagement of International Ranking Agencies, Tashkent State University of Economics, Tashkent, 100066, Uzbekistan Samariddin Makhmudov Department of Tourism and Hotel Management, Bukhara State University, Bukhara, 200100, Uzbekistan Gavkhar Khidirova Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India K. Shankar The most efficient device for modelling uncertainty in decision-making issues is the neutrosophic set (NS) and its add-ons, such as NS of complex, interval, and interval complex. An efficient device for establishing uncertainty in decision-making by inserting three grades of truth, indeterminacy, and falsehood of an established statement. Recently, financial globalization has significantly expanded various methods for enhancing service quality using advanced resources. The practical application of the blockchain (BC) model enables stakeholders concerned about the hazard and return prediction models of economic products. To explore the application of deep learning (DL) in processing financial trading data, a neural network (NN) and DL data are utilized. Absolute stock indices and financial data are utilized for analyzing the efficiency of these models in financial prediction and analysis. This paper presents an Enhanced Risk Prediction Framework for Financial Transactions System Using Interval Neutrosophic Covering Rough Sets (ERPFFTS-INCRS) model. The aim is to develop an effective risk prediction model that enhances the reliability and security of BC financial transactions under uncertain conditions, utilizing neutrosophic logic. Initially, the z-score standardization method is used to clean, transform, and organize raw data into a structured and meaningful format. Furthermore, the ERPFFTS-INCRS method implements the INCRS method for the financial classification process. Finally, the hyperparameter selection for the INCRS model is performed by implementing the Elephant Herding Optimisation (EHO) algorithm. The experimental evaluation of the ERPFFTS-INCRS approach is examined under the metaverse financial transactions (MFT) dataset. The comparison analysis of the ERPFFTS-INCRS approach revealed a superior accuracy value of 98.77% compared to existing methods. 2026 2026 111 124 10.54216/IJNS.270111 https://www.americaspg.com/articleinfo/21/show/3965