Effective Data Classification using Interval Neutrosophic Covering Rough Sets based on Neighborhoods for FinTech Applications
Authors:   Department of Economics and Management of Elabuga Institute, Kazan Federal University, Kazan, 420008, Russia; Department of Economics and Management, Khorezm University of Economics, Urgench, 220100, Uzbekistan, Maksim Maksim ,   Department of Valuation and Corporate Finance, Moscow University for Industry and Finance Synergy, Moscow, 125315, Russia; Department of Corporate Finance and Corporate Governance, Financial University under the Government of the Russian Federation, Moscow, 125993, Russia,   Institute of Digital Technologies and Law, Kazan Innovative University named after V. G. Timiryasov, Kazan, 420111, Russia; Department of International Relations Political Science and Regional Studies, South Ural State University (National Research University), Chelyabinsk, 454080, Russia,   Department of Management, Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, 350044, Russia,   Faculty of Oil and Gas Fields Development, Gubkin Russian State University of Oil and Gas, Moscow, 119991, Russia, Kosorukova Irina , Denisovich Veronika , Klochko Elena , Dengaev Alexey 
Published: October 2024

Neutrosophic set (NS) is particularly appropriate in applications where data is incomplete, unclear, or inconsistent, which offers an effectual means for analyzing and exhibiting complex mechanisms. An NS is a mathematical technique to manage uncertainty, indeterminacy, and imprecision. It enlarges classical sets, IF sets, and fuzzy sets by presenting three degrees such as indeterminacy (I), false (F), and truth (T). Financial technology (Fintech) plays an essential part in advancing modern society, technology, economies, and various fields. Financial crisis prediction (FCP) plays a crucial role in shaping economic outcomes. Past research has predominantly focused on using deep learning (DL), machine learning (ML), and statistical methods to forecast the financial stability of business. In this manuscript, we focus on the development of Effective Data Classification using Interval Neutrosophic Covering Rough Sets based on Neighborhoods and Multi-Strategy Improved Butterfly Optimization (EDCINCRS-MSIBO) Algorithm for FinTech Applications. It contains distinct kinds of stages such as data normalization, feature selection, classification, and parameter tuning. In the EDCINCRS-MSIBO technique, a min-max normalization-based data pre-processing model to scale the raw data into a uniform format. For feature subset selection, the whale optimizer algorithm (WOA) is employed to reduce the dimensionality and improve model efficiency by selecting the most relevant features. In addition, the EDCINCRS-MSIBO technique takes place interval neutrosophic covering rough sets (INCRS) classifier is utilized for detection and classification of a financial crisis. Finally, a multi-strategy improved butterfly optimization algorithm (MSIBOA) is exploited for the optimum parameter adjustment of the INCRS model. To confirm the better predictive solution of the EDCINCRS-MSIBO model, a wide range of simulations are executed on the two benchmark databases. The comparative result analysis displays the encouraging outcomes of the EDCINCRS-MSIBO method on the existing techniques