Interval type-2 fuzzy multi-attribute decision-making approaches for evaluating the service quality of Chinese commercial banks

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In today’s world, with increased competition, the service quality of Chinese commercial banks is recognized as a major factor that is responsible for enhancing competitiveness. Therefore, it is necessary to evaluate and analyse the service quality of Chinese commercial banks to realize their stable development. The service quality evaluation could be recognized as a multi-attribute decision-making (MADM) problem with multiple assessment attributes, both being of a qualitative and quantitative nature. Owing to the growing complexity and high uncertainty of the financial environment, the assessments of attributes cannot always possibly express using a real and/or type-1 fuzzy number. Additionally, a heterogeneous relationship often exists among the attributes under many real decision cases. In this study, we create two MADM approaches to handle decision-making problems with interval type-2 fuzzy numbers (IT2FNs) and offer their application to service quality evaluations of commercial banks problems. Specifically, we first define some operations on IT2FNs based on Archimedean T-norms (ATs) and develop a bi-directional projection measure of IT2FNs. Next, by combining the generalized Banzhaf index, the Choquet integral and IT2FNs, we propose the interval type-2 fuzzy Archimedean Choquet (IT2FAC) operator, the Banzhaf IT2FAC (BIT2FAC) operator and the 2-additive BIT2FAC (2ABIT2FAC) operator. Then, we establish two optimal models for deriving the weights of attributes based on a bi-directional projection measure of IT2FNs and Banzhaf function. Finally, we create two novel MADM methods under interval type-2 fuzzy contexts, where an illustrative case concerning the service quality evaluation of Chinese commercial banks is used to explain the created MADM approaches

A big data analytics method for assessing creditworthiness of SMEs:Fuzzy equifinality relationships analysis

Nowadays, many financial institutions are beginning to use Big Data Analytics (BDA) to help them make better credit underwriting decisions, especially for small and medium-sized enterprises (SMEs) with limited financial histories and other information. The various complexities and the equifinality problem of Big Data make it difficult to apply traditionalstatistical techniques to creditworthiness evaluation, or credit scoring. In this study, we extend the existing research in the field of creditworthiness assessment and propose a novel approach based on neighborhood rough sets (NRSs), to evaluate and investigate the complexities and fuzzy equifinality relationships in the presence of Big Data. We utilize a real SME loan dataset from a Chinese commercial bank to generate interval number rules that provide insight into the fuzzy equifinality relationships between borrowers’ demographic information, company financial ratios, loan characteristics, other non-financial information, local macroeconomic indicators and rated creditworthiness level. In addition, the interval number rules are used to predict creditworthiness levels based on test data and the accuracy of the prediction is found to be 75.44%. One of the major advantages of using the proposed BDA approach is that it helps us to reduce complexity and identify equivalence relationships when using Big Data to assess the creditworthiness of SMEs. This study also provides important implications for practices in financial institutions and SMEs

Assessing and predicting small industrial enterprises’ credit ratings:A fuzzy decision-making approach

Corporate credit-rating assessment plays a crucial role in helping financial institutions make their lending decisions and in reducing the financial constraints of small enterprises. This paper presents a new approach for small industrial enterprises’ credit-rating assessment using fuzzy decision-making methods, and tests it using real bank loan data from 1,820 small industrial enterprises in China. The procedure of the proposed rating approach includes (1) using triangular fuzzy numbers to quantify the qualitative evaluation indicators; (2) adopting a correlation analysis, univariate analysis and stepping backwards feature selection method to select the input features; (3) employing the best-worst method (BWM) combined with the entropy weight method (EWM), the fuzzy c-means algorithm and the technique for order of preference by similarity to ideal solution (TOPSIS) to classify small enterprises into rating classes; and (4) applying the lattice degree of nearness to predict a new loan applicant’s rating. We also conduct a 10-fold cross-validation to evaluate the predictive performance of our proposed approach. The predictive results demonstrate that our proposed data-processing and feature selection approaches have better accuracy than the alternative approaches in predicting default, offering bankers a new valuable rating system to assist their decision making

Fuzzy Approach in Ranking of Banks according to Financial Performances

Evaluating bank performance on a yearly basis and making comparison among banks in certain time intervals provide an insight into general financial state of banks and their relative position with respect to the environment (creditors, investors, and stakeholders). The aim of this study is to propose a new fuzzy multicriteria model to evaluate banks respecting relative importance of financial performances and their values. The relative importance of each pair of financial performance groups is assessed linguistic expressions which are modeled by triangular fuzzy numbers. Fuzzy Analytic Hierarchical Process (FAHP) is applied to determine relative weights of the financial performances. In order to rank the treated banks, new model based on Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) is deployed. The proposed model is illustrated by an example giving real life data from 12 banks having 80% share of the Serbian market. In order to verify the proposed FTOPSIS different measures of separation are used. The presented solution enables the ranking of banks, gives an insight of bank's state to stakeholders, and provides base for successful improvement in a field of strategy quality in bank business

Fuzzy Approach in Ranking of Banks according to Financial Performances

Evaluating bank performance on a yearly basis and making comparison among banks in certain time intervals provide an insight into general financial state of banks and their relative position with respect to the environment (creditors, investors, and stakeholders). The aim of this study is to propose a new fuzzy multicriteria model to evaluate banks respecting relative importance of financial performances and their values. The relative importance of each pair of financial performance groups is assessed linguistic expressions which are modeled by triangular fuzzy numbers. Fuzzy Analytic Hierarchical Process (FAHP) is applied to determine relative weights of the financial performances. In order to rank the treated banks, new model based on Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) is deployed. The proposed model is illustrated by an example giving real life data from 12 banks having 80% share of the Serbian market. In order to verify the proposed FTOPSIS different measures of separation are used. The presented solution enables the ranking of banks, gives an insight of bank's state to stakeholders, and provides base for successful improvement in a field of strategy quality in bank business

Partner selection in sustainable supply chains: a fuzzy ensemble learning model

With the increasing demands on businesses to operate more sustainably, firms must ensure that the performance of their whole supply chain in sustainability is optimized. As partner selection is critical to supply chain management, focal firms now need to select supply chain partners that can offer a high level of competence in sustainability. This paper proposes a novel multi-partner classification model for the partner qualification and classification process, combining ensemble learning technology and fuzzy set theory. The proposed model enables potential partners to be classified into one of four categories (strategic partner, preference partner, leverage partner and routine partner), thereby allowing distinctive partner management strategies to be applied for each category. The model provides for the simultaneous optimization of both efficiency in its use of multi-partner and multi-dimension evaluation data, and effectiveness in dealing with the vagueness and uncertainty of linguistic commentary data. Compared to more conventional methods, the proposed model has the advantage of offering a simple classification and a stable prediction performance. The practical efficacy of the model is illustrated by an application in a listed electronic equipment and instrument manufacturing company based in southeastern China

Bibliometric analysis of scientific production on methods to aid decision making in the last 40 years

Purpose: Multicriteria methods have gained traction in both academia and industry practices for effective decision-making over the years. This bibliometric study aims to explore and provide an overview of research carried out on multicriteria methods, in its various aspects, over the past forty-four years. Design/Methodology/Approach: The Web of Science (WoS) and Scopus databases were searched for publications from January 1945 to April 29, 2021, on multicriteria methods in titles, abstracts, and keywords. The bibliographic data were analyzed using the R bibliometrix package. Findings: This bibliometric study asserts that 29,050 authors have produced 20,861 documents on the theme of multicriteria methods in 131 countries in the last forty-four years. Scientific production in this area grows at a rate of 13.88 per year. China is the leading country in publications with 14.14%; India with 10.76%; and Iran with 8.09%. Islamic Azad University leads others with 504 publications, followed by the Vilnius Gediminas Technical University with 456 and the National Institute of Technology with 336. As for journals, Expert Systems With Applications; Sustainability; and Journal of Cleaner Production are the leading journals, which account for more than 4.67% of all indexed literature. Furthermore, Zavadskas E. and Wang J have the highest publications in the multicriteria methods domain regarding the authors. Regarding the most commonly used multicriteria decision-making methods, AHP is the most favored approach among the ten countries with the most publications in this research area, followed by TOPSIS, VIKOR, PROMETHEE, and ANP. Practical implications: The bibliometric literature review method allows the researchers to explore the multicriteria research area more extensively than the traditional literature review method. It enables a large dataset of bibliographic records to be systematically analyzed through statistical measures, yielding informative insights. Originality/value: The usefulness of this bibliometric study is summed in presenting an overview of the topic of the multicriteria methods during the previous forty-four years, allowing other academics to use this research as a starting point for their research

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Usage of Structural Equation Modeling and Analytical Hierarchy Process Approach to Select Information Technology

Progressive change is an accurate way to describe the advancement of information technology (IT) throughout the 1990s. As IT continues to evolve, the ways in which companies do business are also changing. The emergence ofthe Internet as a business venue, the growing percentage of consumers accessing the Web, and the increasing number of households equipped with a PC or other Web-access device are speeding IT's rate of change. The industries especially banking and financial services industries (BFSI) are heavily supported by IT and technology vendor for their service oriented business. It indicates that choosing the right vendor remains a critical success factor for every enterprise's business success. Selection of the best possible set of vendors not only allow organisations to downsize and utilise resources more effectively, but also allows themto take advantage of the capabilities andtechnologies of the vendors. The vendor selection process can be a very complicated and emotional undertaking if the approach from the very beginning is not known. The purpose ofthis research is to identify the required criteria for selecting the best vendor for information technology (IT) process and provide a vendor selection model including these criteria by using the structural equation modeling (SEM) and analytic hierarchy process (AHP). To demonstrate the above model and also, to arrive at vendor selection scores, the vendor selection for mobile banking application was considered as an example. The developed model is a generic one considering the global economic turmoil and the amount ofpressure on banking &financial services industries (BFSI), where IT is the backbone of the BFSI; In any future studies the model could be applied in making other strategic decisions like IT outsourcing, ERP (enterprise resource planning) implementation vendor selection etc. V