aspg-21-2784-1791465337 20261008131537 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2024 23 4 Fadoua Kouki Department of Financial and Banking Sciences, Applied College at Muhail Aseer, King Khalid University, Saudi Arabia Financial technology (Fintech) is paramount in driving advanced technologies, economies, society, and several other sectors. Smart Fintech is the new-generation Fintech, primarily stimulated and endowed by compuational technology. Smart Fintech syndicates DSAI and renovates economies and finance for dynamic, smart, customized, automated services and systems, economies and financial companies, and the industry. The strength and development of the country’s economies are assessed by the correct forecasting. Financial crisis prediction (FCP) has the substantial consequence on the economies. Previous studies mainly emphasise statistical, DL, and ML methodologies for predicting the financial well-being of the business. Therefore, this article develops a new Arithmetic Optimization Algorithm with Adaptive Neuro-Fuzzy Interference System (AOA-ANFIS) technique for Predicting Financial Crisis. The presented AOA-ANFIS technique aims to predict the presence of financial crises or not. The model incorporates three major elements: Arithmetic Optimization Algorithm (AOA) for feature selection, Adaptive Neuro-Fuzzy Inference System (ANFIS) as the classification algorithm, and Bat Optimization Algorithm (BOA) for parameter tuning. The AOA feature selection model effectively detects the important attributes from a large proportion of financial indicators, augmenting the model's prediction capability while decreasing computational difficulty. Subsequently, the ANFIS classifier exploits the features selected for capturing the intricate non-linear relations intrinsic in financial data, permitting accurate crisis calculation. Additionally, the BOA parameter tuning model augments the ANFIS model's parameters, ensuring robustness and optimum performance. Experimental outcomes on varied financial databases validate the higher efficiency of the AOA-ANFIS technique over underlying processes, demonstrating its effectiveness in forecasting financial crises with great reliability and precision. 2024 426 438 2784 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.230435 https://www.americaspg.com/journal/21/article/2784 https://www.americaspg.com/storage/31715264893.pdf