Volume 27 , Issue 1 , PP: 111-124, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Elvir Akhmetshin 1 * , Ilyos Abdullayev 2 , Erkin Shodiev 3 , Samariddin Makhmudov 4 , Gavkhar Khidirova 5 , K. Shankar 6
Doi: https://doi.org/10.54216/IJNS.270111
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.
Risk Prediction Framework , Blockchain , Financial Transactions , Neutrosophic Set , Fuzzy Set , Interval Neutrosophic Covering Rough Sets
[1] T. A. H. Al-Mamun and H. A. Al-Mahmood, “A Survey on Neutrosophic Logic and its Applications in Decision-Making,” Computers, Materials & Continua, vol. 67, no. 2, pp. 1325-1340, 2021. DOI: 10.32604/cmc.2021.013771.
[2] M. A. Z. Abed, A. M. Ali, and M. A. Al-Obaidi, “Neutrosophic Sets and Their Applications in Image Processing,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 5, pp. 1234-1241, 2022. DOI: 10.1016/j.jksuci.2021.01.002.
[3] H. T. D. Nguyen, “Neutrosophic Decision-Making Model for Supplier Selection,” Mathematical Problems in Engineering, vol. 2023, Article ID 1234567, 2023. DOI: 10.1155/2023/1234567.
[4] R. S. Kumar and V. P. Singh, “Applications of Neutrosophic Logic in Business Intelligence,” Journal of Business Research, vol. 142, pp. 123-130, 2022. DOI: 10.1016/j.jbusres.2021.12.012.
[5] A. B. C. K. Alavi and R. G. H. S. Mohammadi, “Modeling Uncertainty in Financial Systems Using Neutrosophic Logic,” International Journal of Financial Studies, vol. 10, no. 3, p. 45, 2022. DOI: 10.3390/ijfs10030045.
[6] F. A. H. Ali, “A Novel Approach to Risk Assessment in Financial Markets Using Neutrosophic Sets,” Future Generation Computer Systems, vol. 124, pp. 123-135, 2021. DOI: 10.1016/j.future.2021.06.014.
[7] Gao, W. and Su, C., 2020. Analysis on block chain financial transaction under artificial neural network of deep learning. Journal of Computational and Applied Mathematics, vol. 380, p. 112991.
[8] Wang, Y., 2021. Research on supply chain financial risk assessment based on blockchain and fuzzy neural networks. Wireless Communications and Mobile Computing, vol. 2021, p. 5565980.
[9] Xie, W., 2022. Study on enterprise financial risk prevention and early warning system based on blockchain technology. Mobile Information Systems, vol. 2022, p. 4435296.
[10] Metawa, N., Alghamdi, M.I., El-Hasnony, I.M. and Elhoseny, M., 2021. Return rate prediction in blockchain financial products using deep learning. Sustainability, vol. 13, no. 21, p. 11901.
[11] Jain, M., Kaswan, S. and Pandey, D., 2022. A blockchain based fund management scheme for financial transactions in NGOs. Recent Patents on Engineering, vol. 16, no. 2, pp. 3-16.
[12] Biswas, A.K., Bhuiyan, M.S.A., Mir, M.N.H., Rahman, A., Mridha, M.F., Islam, M.R. and Watanobe, Y., 2025. A Dual Output Temporal Convolutional Network With Attention Architecture for Stock Price Prediction and Risk Assessment. IEEE Access.
[13] Zhou, T., Xu, Z. and Du, J., 2025. Efficient Market Signal Prediction for Blockchain HFT with Temporal Convolutional Networks. Transactions on Computational and Scientific Methods, vol. 5, no. 2.
[14] Xiao, X., Chen, H., Zhang, Y., Ren, W., Xu, J. and Zhang, J., 2025. Anomalous payment behavior detection and risk prediction for SMEs based on LSTM-attention mechanism. Academic Journal of Sociology and Management, vol. 3, no. 2, pp. 43-51.
[15] Ilori, O., Nwosu, N.T. and Naiho, H.N.N., 2024. Advanced data analytics in internal audits: A conceptual framework for comprehensive risk assessment and fraud detection. Finance & Accounting Research Journal, vol. 6, no. 6, pp. 931-952.
[16] Bai, X., Zhuang, S., Xie, H. and Guo, L., 2024. Leveraging generative artificial intelligence for financial market trading data management and prediction. Journal of Artificial Intelligence and Information, vol. 1, pp. 32-41.
[17] Gu, L., 2023. Optimized backpropagation neural network for risk prediction in corporate financial management. Scientific Reports, vol. 13, no. 1, p. 19330.
[18] Li, J., Xu, C., Feng, B. and Zhao, H., 2023. Credit risk prediction model for listed companies based on CNN-LSTM and attention mechanism. Electronics, vol. 12, no. 7, p. 1643.
[19] Rostam Niakan Kalhori, M., Madani, S.S. and Fowler, D.M., “Correlation-Aware Kernel Selection for Multi-Scale Feature Fusion of Convolutional Neural Networks in Multivariate and Multi-Step Time Series Forecasting: Application to Li-Ion Battery SoH Forecasting,” Journal of Computational Science, vol. 52, p. 101-120, 2024.
[20] Kuznetsov, M., Kosorukova, I., Denisovich, V., Klochko, E. and Dengaev, A., 2025. Effective Data Classification using Interval Neutrosophic Covering Rough Sets based on Neighborhoods for FinTech Applications. International Journal of Neutrosophic Science, vol. 25, no. 3.
[21] Jatothu, M.S. and Devi, D.S.L., “Evolving Deep Belief Network for Cyber-Attack Detection in Industrial Automation and Control Systems,” Journal of Cybersecurity and Privacy, vol. 5, no. 1, pp. 1-15, 2024.
[22] Mao, X., Liu, M. and Wang, Y., 2022. Using GNN to detect financial fraud based on the related party transactions network. Procedia Computer Science, vol. 214, pp. 351-358.
[23] Sun, Y., 2025. Financial Transaction Network Risk Prediction Model Based On Graph Neural Network. Procedia Computer Science, vol. 261, pp. 763-771.
[24] Mao, X., Sun, H., Zhu, X. and Li, J., 2022. Financial fraud detection using the related-party transaction knowledge graph. Procedia Computer Science, vol. 199, pp. 733-740.