430 research outputs found

    Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping:a case of Mozambican banks

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    Performance analysis has become a vital part of the management practices in the banking industry. There are numerous applications using DEA models to estimate efficiency in banking, and most of them assume that inputs and outputs are known with absolute precision. Here, we propose new Fuzzy-DEA α-level models to assess underlying uncertainty. Further, bootstrap truncated regressions with fixed factors are used to measure the impact of each model on the efficiency scores and to identify the most relevant contextual variables on efficiency. The proposed models have been demonstrated using an application in Mozambican banks to handle the underlying uncertainty. Findings reveal that fuzziness is predominant over randomness in interpreting the results. In addition, fuzziness can be used by decision-makers to identify missing variables to help in interpreting the results. Price of labor, price of capital, and market-share were found to be the significant factors in measuring bank efficiency. Managerial implications are addressed

    The Colombian Banking Sector: Analysis from Relative Efficiency

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    The banking sector is that sector of the modern economy that is primarily called upon to play the important role of intermediation between the surplus agents and the deficit agents. Based on this fact, this research presents and analyzes the behavior of banks in Colombia since 2002 and up to 2016 (15 years) through the application of data envelopment analysis, a nonparametric methodology of advanced linear programming, which generates a single efficiency indicator for each unit studied in each period, optimizing multiple resources (inputs) and multiple products (outputs). One aspect of the results shows that for the year 2014, 71% of the banks were efficient, this being the highest result within the period studied

    Carbon efficiency evaluation:an analytical framework using fuzzy DEA

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    Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers

    Defuzzification of groups of fuzzy numbers using data envelopment analysis

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    Defuzzification is a critical process in the implementation of fuzzy systems that converts fuzzy numbers to crisp representations. Few researchers have focused on cases where the crisp outputs must satisfy a set of relationships dictated in the original crisp data. This phenomenon indicates that these crisp outputs are mathematically dependent on one another. Furthermore, these fuzzy numbers may exist as a group of fuzzy numbers. Therefore, the primary aim of this thesis is to develop a method to defuzzify groups of fuzzy numbers based on Charnes, Cooper, and Rhodes (CCR)-Data Envelopment Analysis (DEA) model by modifying the Center of Gravity (COG) method as the objective function. The constraints represent the relationships and some additional restrictions on the allowable crisp outputs with their dependency property. This leads to the creation of crisp values with preserved relationships and/or properties as in the original crisp data. Comparing with Linear Programming (LP) based model, the proposed CCR-DEA model is more efficient, and also able to defuzzify non-linear fuzzy numbers with accurate solutions. Moreover, the crisp outputs obtained by the proposed method are the nearest points to the fuzzy numbers in case of crisp independent outputs, and best nearest points to the fuzzy numbers in case of dependent crisp outputs. As a conclusion, the proposed CCR-DEA defuzzification method can create either dependent crisp outputs with preserved relationship or independent crisp outputs without any relationship. Besides, the proposed method is a general method to defuzzify groups or individuals fuzzy numbers under the assumption of convexity with linear and non-linear membership functions or relationships

    A novel modified Khatter’s approach for solving Neutrosophic Data Envelopment Analysis

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    The evaluation of the performance of decision-making units (DMUs) that use comparable inputs to produce related outputs can be accomplished through a non-parametric linear programming (LP) technique called Data Envelopment Analysis (DEA). However, the observed data are occasionally imprecise, ambiguous, inadequate, and inconsistent which may result in incorrect decision-making when these criteria are ignored. Neutrosophic Set (NS) is an extension of fuzzy sets which is used to represent unclear, erroneous, missing, and wrong information. This paper proposes a neutrosophic version of the DEA model, and a novel solution technique for Neutrosophic DEA (Neu-DEA) model. The possibility mean for triangular neutrosophic number (TNN) is redefined and modified the Khatter’s approach to convert directly the Neu-DEA model into its crisp DEA model. As a result, the Neu-DEA model is simplified to a crisp LP problem with a risk parameter (δ ∈ [0, 1]) that represents the attitude of the decision-maker towards taking risk. The efficiency score of the DMUs is computed by using various risk factors and divided into efficient and inefficient groups. The ranking of DMUs is determined by calculating the mean efficiency score of DMUs, which is based on various risk parameters. A numerical example is illustrated here to describe the suggested approach’s flexibility and authenticity and compared with some of the existing approaches

    Operational research and artificial intelligence methods in banking

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    Supplementary materials are available online at https://www.sciencedirect.com/science/article/pii/S037722172200337X?via%3Dihub#sec0031 .Copyright © 2022 The Authors. Banking is a popular topic for empirical and methodological research that applies operational research (OR) and artificial intelligence (AI) methods. This article provides a comprehensive and structured bibliographic survey of OR- and AI-based research devoted to the banking industry over the last decade. The article reviews the main topics of this research, including bank efficiency, risk assessment, bank performance, mergers and acquisitions, banking regulation, customer-related studies, and fintech in the banking industry. The survey results provide comprehensive insights into the contributions of OR and AI methods to banking. Finally, we propose several research directions for future studies that include emerging topics and methods based on the survey results

    An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

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    To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.This article belongs to the Special Issue Sustainability Assessmen

    Energy Use Efficiency

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    Energy is one of the most important factors of production. Its efficient use is crucial for ensuring production and environmental quality. Unlike normal goods with supply management, energy is demand managed. Efficient energy use—or energy efficiency—aims to reduce the amount of energy required to provide products and services. Energy use efficiency can be achieved in situations such as housing, offices, industrial production, transport and agriculture as well as in public lighting and services. The use of energy can be reduced by using technology that is energy saving. This Special Issue is a collection of research on energy use efficiency
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