International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/3187 2020 2020 Department of Administrative Sciences, Applied College, Abha, King Khalid University, Saudi Arabia Halla Halla Department of Administrative Sciences, Applied College, Khamis Mushait, King Khalid University, Saudi Arabia Abdelgalal O. I. Abaker Department of Computer Applications, Faculty of Science & Humanities, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India Elavarasi Gunasekaran Department of Business Administration, Faculty of Science and Humanity Studies in Al-Sulail, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia Hago E. M. Ali Department of Finance and Banking, College of Business Administration, Imam Abdulrahman bin Faisal University, DAMMAM 32444, Saudi Arabia Abdullah S. Alharbi Department of Mathematics, Turabah University College, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Amer Alsulami Department of Mathematics and Statistics, College of Science, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia Azhari A Elhag Neutrosophic set (NS) is a prevailing logic aimed at facilitating the understanding of inconsistent and indeterminate data; several kinds of complete or incomplete data can be described as interval-valued NS (IVNS). This study presents aggregation operator for IVNSs and prolongs the generalized weighted aggregation (GWA) operations to congruently work with IVNS information. Also, these results are formulated as IVNSs that are represented by indeterminate, truth, and false degrees. The tremendous growth of financial innovation offers a several convenience to people’s lives and production and brings many security risks to financial technology. To avoid financial risk, an improved way is to construct an accurate warning mechanism before the financial risk takes place, not to solve this matter after the risk outbreak. Recently, deep learning (DL) has delivered outstanding results in the natural language processing and image recognition areas. Thus, researcher used DL techniques for the financial risk prediction and obtained satisfactory results. This study develops a new Pythagorean Neutrosophic Normal Interval-Valued Weighted Averaging for Financial Risk Prediction (PNNIVWA-FRP) method using sustainable development. The objective of the PNNIVWA-FRP method is to have two dissimilar stages of processes. Initially, financial data are classified by the PNSNIVWA technique. This method is used for its highest proficiency in managing imprecision and uncertainty in financial data, containing incomplete and ambiguous data. Second, the classified parameter is fine-tuned by means of Glowworm Swarm Optimization (GSO) technique. Based on the luminescent communication of glowworms, GSO is proficient at navigating multidimensional, complex search spaces for identifying better solutions. The empirical findings on benchmark dataset demonstrate the effectiveness of the PNNIVWA-FRP method, showcasing significant development in prediction results than classical approaches. 2025 2025 279 289 10.54216/IJNS.250224 https://www.americaspg.com/articleinfo/21/show/3187