AI-based model for Enhancing Credit Risk and Delinquency Management in Banks

Noura; Sally; Nasser

doi:https://doi.org/10.54216/JCIM.160120

International Journal of Neutrosophic Science | 2025

An Investigation of Complex Linear Diophantine Fuzzy Ideals in BCK-Algebras

A complex linear Diophantine fuzzy (CLDF) set extends a linear Diophantine fuzzy set (LDFS) by handling uncertainty with complex-valued membership degrees within a unit disc. In this paper, we combine the notions of LDFS, BCK-algebra, and complex fuzzy set (CFS) to preface and elaborate the innovative concepts of CLDF subalgebras (CLDF − Subs), CLDF ideals (CLDF − Ids), CLDF implicative ideals (CLDF − IIds), and CLDF positive implicative ideals (CLDF − PIIds) in BCK-algebras, and probe their fundamental characteristics. These new notations of certain kinds of algebraic substructures in BCK-algebras serve as a bridge among CLDFS, crisp set, and BCK-algebra and also demonstrate the influence of the CLDFS on a BCK-algebra. Moreover, we examine some illustrative examples and prevalent features of these innovative notions in detail. Finally, characterizations of these intricate fuzzy structures are given, and related results for ideals, implicative ideals, and positive implicative ideals in the view of CLDFSs are obtained.

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Anas Al-Masarwah mail -

Manivannan Balamurugan mail -

Thukkaraman Ramesh mail -

Majdoleen Abuqamar mail -

Maryam Abdullah Alshayea mail

link https://doi.org/10.54216/IJNS.260303

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Fuzzy Bounded Linear Operators on Fuzzy Anti-Normed Spaces

The primary goal of this paper is to study and introduce fuzzy anti-normed linear spaces, as well as, some additional properties concerning these spaces. From this point of view, some theoretical results are obtained; for example, it was proved that the space of all linear and fuzzy bounded operators over fuzzy anti-normed linear spaces is fuzzy complete. Moreover, some additional theoretical results are stated and proved.

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Jaafer Hmood Eidi mail -

Aamena Al-Qabani mail -

Fadhel S. Fadhel mail -

Jehad R. Kider mail

link https://doi.org/10.54216/IJNS.260304

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Bipolar Fuzzy Hypersoft Set with Heuristic Search Based Customer Retention Prediction Model in Financial Sectors

From a philosophical viewpoint, the theory of neutrosophic set (NS) is a simplification of the concept of Fuzzy Set (FS) and intuitionistic FS (IFS). An NS is illustrated by a truth, an indeterminacy, and a falsity membership functions and every membership degree is an actual standard or a non-standard sub-set of the non-standard unit range of] −0, 1+ [.  Customer churn is when clients stop utilizing a company’s service or product. Moreover, it is also named customer retention, which is vastly significant metric as it is much less costly to keep the existing customers than to obtain novel customers. The prediction of churn plays an essential part in customer retention because it forecasts clients who are in danger of leaving the organization. In the banking sector, the customer attrition arises when clients quit utilizing the services and goods provided by the bank for some time. So, customer churn is vital in today’s economic banking industry. This study proposes a Leveraging Bipolar Fuzzy Hypersoft Set with Heuristic Optimization Algorithms-based Customer Retention Prediction (BFHSS-HOACRP) technique in financial sectors. The BFHSS-HOACRP technique applies optimized techniques to predict the customer retention behavior in the industry of bank.  Initially, the mean normalization technique is utilized in the data pre-processing stage to prepare raw data into a suitable format for analysis and modeling. For the selection of feature process, the grasshopper optimization algorithm (GOA) method is employed to identify and select the most relevant features from an input data. In addition, the proposed BFHSS-HOACRP technique implements bipolar fuzzy hypersoft set (BFHSS) method for the classification process. Additionally, the spider monkey optimization (SMO)-based hyperparameter selection process is performed to optimize the classification results of BFHSS model. The efficacy of the BFHSS-HOACRP approach is examined under the bank customer churn prediction dataset. The comparison analysis of the BFHSS-HOACRP approach portrayed a superior accuracy value of 95.41% over existing techniques.

groups

Alexander Kalinin mail -

Inomjon Yusubov mail -

Tatiana Yakubova mail -

Victoria Kruglyakova mail -

Tatyana Khorolskaya mail

link https://doi.org/10.54216/IJNS.260305

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Pentapartitioned Neutrosophic Vague Soft Set with Optimization Algorithm Based Business Intelligence Framework for Data-Driven Demand Forecasting Model

Neutrosophic logic is a neonate research field in which all propositions are anticipated to have the percentage (proportion) of truth in a sub-set T, the proportion of falsity in a sub-set F, and the proportion of indeterminacy in a sub-set I. Neutrosophic set (NS) is efficiently applied for indeterminate information processing and provides assistance to address the indeterminacy information of data. Demand Forecasting, undoubtedly, is the only most significant element of some organization's Supply Chain. It defines the predictable demand for the future and sets the preparedness level that is needed on the supply side to match the demand. Business intelligence (BI) plays a significant part in helping the decision maker obtain the understanding for increasing productivity or improved and faster decisions. Furthermore, it improves and helps the efficacy of functional rules and its influence on corporate-level decision-making that provides improved strategic options in dynamic business environments. Within the period of data-driven demand forecasting, the integration of artificial intelligence (AI) technologies in BI models has transformed the system groups that utilize and analyze data. In the manuscript, a Business Intelligence Framework for a Data-Driven Demand Forecasting Model Using a Pentapartitioned Neutrosophic Vague Soft Set (BIFDDF-PNVSS) technique is proposed. The main goal of the BIFDDF-PNVSS technique is to progress the accurate BI structure for the demand forecasting method. The data pre-processing stage is initially applied for converting input data into a beneficial format by the Z-score normalization method. Moreover, the PNVSS technique is utilized for the data-driven demand prediction model. Finally, to improve the prediction performance of the PNVSS model, the parameter tuning process is performed by implementing the cheetah optimization algorithm (COA) model. A comprehensive experimentation is performed to verify the performance of the BIFDDF-PNVSS methodology under the demand forecasting dataset. The BIFDDF-PNVSS methodology outperforms existing techniques with a superior MSE of 0.0008, demonstrating its exceptional accuracy in demand forecasting compared to other models.

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Sanat Chuponov mail -

Tukhtabek Rakhimov mail -

Natalya Shcherbakova mail -

Vladimir Kurikov mail -

Olga Berezhnykh mail -

K. Shankar mail

link https://doi.org/10.54216/IJNS.260306

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Neutrosophic Fusion Based Rough Set Theory for Intelligent Decision Support System on Business-to-Business (B2B) Sales Estimation

One of the most effective devices to model uncertainty in decision-making difficulties is the Neutrosophic set (NS) and its extensions, like interval NS (INS), interval complex NS (ICNS), and complex NS (CNS). Predicting the result of sales benefits is the essential element of effective business management. Traditionally, undertaking this prediction has depended generally on individual human analyses in the sales decision-making process. A model of business-to-business (B2B) sales predicting is a difficult decision-making procedure. There are several methods for supporting this procedure; however, generally it is even established on the individual judgments of the decision-maker. The B2B sales predicting problem is represented as the prediction problem. Presently, intelligible predictive methods were analyzed and studied utilizing the technique of machine learning (ML) to increase the upcoming sales prediction. This paper presents an Adaptive Intelligent Business to Business Sales Estimation using Neutrosophic Fusion of Rough Set Theory (AIB2BSE-NFRST) model. The main intention of AIB2BSE-NFRST technique is to enhance prediction analysis for B2B sales estimation using advanced techniques. Initially, the data pre-processing performs min-max normalization to prepare raw input data for analysis by transforming it into a structured format. Furthermore, the proposed AIB2BSE-NFRST technique utilizes NFRST method for the prediction process. To further optimize model performance, the seagull optimization algorithm (SOA) is utilized for hyperparameter tuning to ensure that the best hyperparameter is selected. To exhibit the enhanced performance of the presented AIB2BSE-NFRST model, a comprehensive experimental analysis is made under the E-commerce sales dataset. The AIB2BSE-NFRST model outperforms existing techniques with a superior MSE of 0.0033, highlighting its exceptional accuracy in B2B sales estimation.

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Elvir Akhmetshin mail -

Ilyos Abdullayev mail -

Irina Gladysheva mail -

Emil Hajiyev mail -

Elena Klochko mail

link https://doi.org/10.54216/IJNS.260307

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Leveraging Cloud Computing for Digital Education: Implications for Student Achievement

The research evaluates the effects, which cloud computing and digital educational methods have on scholarly performance. The research used descriptive statistics combined with t-tests alongside ANOVA and regression analysis for interpreting the data findings. The collected data shows students use cloud computing moderately and employ digital education extensively although their educational outcomes stay average. Cloud computing usage exhibited similar levels of acceptance between male and female students however, students from arts streams programs demonstrated increased interest. Cloud computing usage along with digital education experienced superior adoption rates among students residing in rural areas than students settled in urban areas. Research data showed a major statistical linkage between digital education and the levels of academic performance. The educational institution types together with parental work status shaped student interaction with digital educational resources. The study's findings highlight the significant roles played by cloud computing and online learning in raising students' academic performance. The research implies that mixing technology with current education practices will boost educational results while demonstrating why digital competence stands vital in present-day education systems. Academic achievement rates improved in direct proportion to the amount of digital education use by students alongside the fact that private institution students demonstrated higher application of cloud computing platforms and female students demonstrated superior academic outcomes when compared to male students. Numerous students adopt both cloud computing systems and digital education methods because such technology usage is prevalent at accuracy 91.4% of the total students. Out of all the analyses done in the research, the overall F1-score is 92.5, and the fault tolerance of 93.8%.

groups

Nasser El-Kanj mail -

Chadi El Nar mail -

Marina Abdurashidova mail

link https://doi.org/10.54216/JISIoT.160223

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

An Intelligent Financial Risk Management System Using Pythagorean Neutrosophic Fuzzy Graphs with Growth Optimization Algorithm

One of the most effective devices to model uncertainty in decision-making difficulties is the neutrosophic set (NS) and its extensions, namely interval NS (INS), interval complex NS (ICNS), and complex NS (CNS). An effective device to demonstrate ambiguities and uncertainty in decision-making is the NS, which is the more conventional standard set, intuitionistic fuzzy set (IFS), and fuzzy set (FS) by including 3 scores of falsehood, indeterminacy, and truth of established statements. Financial risk management is a massive field with different and developing modules, as demonstrated by either its historic growth or present classic example. It is a procedure to address the uncertainty originating from financial markets. It consists of calculating the financial threats dealing with organization and emerging management tactics by internal policies and priorities. A risk-management method is an experience control and accounting system. In this manuscript, we develop an Intelligent Risk Management Approach for Financial Crisis Using Pythagorean Neutrosophic Fuzzy Graphs and Metaheuristic Optimization Algorithms (IRMFC-PNFGMOA). The main intention of IRMFC-PNFGMOA technique is to analyse and develop effective methodologies for measuring and managing financial risk in dynamic market conditions. Initially, the data pre-processing stage applies Z-score normalization to clean, transform, and structure raw data to improve the quality. Besides, the Aquila optimization algorithm (AOA) has been deployed for the selection of feature processes to identify and retain the most relevant features from input data. For the classification process, the proposed IRMFC-PNFGMOA model designs pythagorean neutrosophic fuzzy graphs (PNFG) technique. To further optimize model performance, the growth optimizer (GO) algorithm is utilized for hyperparameter tuning to ensure that the best hyperparameters are selected for enhanced accuracy. To exhibit the enhanced performance of the presented IRMFC-PNFGMOA model, a comprehensive experimental analysis is made. The comparative results reported the improvised characteristics of the IRMFC-PNFGMOA model.

groups

N. Metawa mail -

Olim Astanakulov mail -

Umarova Navbakhor Shokirovna mail

link https://doi.org/10.54216/JISIoT.160224

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Sustainable Development and Green Technology | 2025

Analysis of Investment Attractiveness of Countries: A Comprehensive Assessment Using Econometric Models

This article analyzes the investment attractiveness of various countries by developing ranking systems and econometric models. These models, based on key economic indicators, evaluate countries' investment potential and provide forecasted values for the Global Innovation Index (GII). Using a weighted scoring method, we rank countries according to their investment attractiveness. The study further constructs an econometric model to explore the relationship between investment factors and innovation development, highlighting key areas for policy improvement.

groups

Eshonkulova Sayyorabonu mail

link https://doi.org/10.54216/JSDGT.050101

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Journal of Sustainable Development and Green Technology | 2025

The Role of E-Commerce Development in Shaping the Global Market Conjuncture

This article explores the transformative role of e-commerce in reshaping the global market landscape. Through an in-depth examination of digital trade, supply chain realignment, consumer behavior, and global economic integration, the study assesses how the development of e-commerce has transcended traditional market boundaries and redefined competition, pricing, and logistics. It evaluates the influence of technological infrastructure, regulatory frameworks, and international cooperation in driving the growth of e-commerce and highlights key challenges, including data security, digital inequality, and market volatility. The article concludes with a critical outlook on the structural shifts e-commerce introduces to the global market conjuncture and its implications for the future of trade and economic policy.

groups

Hamroyeva Umida mail

link https://doi.org/10.54216/JSDGT.050102

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

AI-based model for Enhancing Credit Risk and Delinquency Management in Banks

Credit risk assessment along with delinquency management in banking receives substantial improvements from the introduction of Artificial Intelligence (AI) and behavioural insights. This research creates an extensive behavioural credit-scoring model through its discovery of crucial psychological characteristics including integrity and self-efficacy and locus of control and materialism that greatly affect credit default and wilful delinquency. A thorough evaluation of the predictive model occurs through logistic regression and confirmatory factor analysis (CFA) based analysis on 376 respondent data. Self-efficacy together with internal locus of control and materialism demonstrate significant power as predictors for credit risk and the willingness of individuals to default voluntarily is directly influenced by integrity and self-esteem. The ability of Artificial intelligence approaches to forecasting depends on behavioural constructs to optimize precision accuracy, reduce credit risk estimation errors, and provide opportunities for early prevention. The model delivers 92.1% accurate Default Risk classifications together with 91.0% precise predictions for Liquidity Risk while maintaining a Default Risk AUC-ROC measure of 0.96, which signifies its advanced predictive capabilities. The research demonstrates that artificial intelligence alongside behavioural credit scoring systems can enhance financial lending decisions while stabilizing credit markets.

groups

Noura Metawa mail -

Sally Afchal mail -

Nasser El-Kanj mail

link https://doi.org/10.54216/JCIM.160120

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new