The New Method for Ranking Grouped Credit Customer Based on DEA Method
Authors
Abstract:
Data Envelopment Analysis (DEA) is a widely used non-parametric method for ranking by Decision-Making Units (DMU). Despite the fact that DEA method does not require numerous preconditions, the necessity of the DMUs to be homogeneous is one of the most important rules in applying this technique. Moreover, in real world problems, due to the nature of DMUs, the need for ranking the grouped data has gained significant importance. Credit rating of the financial facility applicants is considered by the banks and financial institutions as one of the most important management issues and significant budget is allocated to develop and imply an effective rating system. Since the applicant organizations operate in different businesses and industries, and simultaneous rating of these companies using the DEA method leads to violation of homogeneity rule, thus, application of this powerful tool is restricted. The purpose of this paper is to resolve this key weakness in such a way that makes it possible to simultaneously consider the heterogeneous companies. The results of the proposed method have shown an enhanced capability for rating the decision-making units
similar resources
The New Method for Credit Customer Selecting by Integration of A2 and Data Envelopment Analysis (A2_DEA)
This paper develops a decision support tool using an A2 method and data envelopment analysis (DEA) approach (A2-DEA). This new method is applied for the bank credit customer selection problem and credit scoring as a pilot survey at Export Development Bank of Iran. The proposed method has led to fewer calculations, faster and more accurate decision making, less complexity, and ability to ana...
full textMaximum appreciative cross-efficiency in DEA: A new ranking method
Ranking decision making units (DMUs) is one of the most important applications of data envelopment analysis (DEA). In this paper, we exploit the power of individual appreciativeness in developing a methodology that combines crossevaluation, preference voting and ordered weighted averaging (OWA). We show that each stage of the proposed methodology enhances discrimination among DMUs while offerin...
full textA New Method for Ranking Distribution Companies with Several Scenarios Data by Using DEA/MADM
In Data Envelopment Analysis, uncertain data are the inseparable part of real models. Natural models usually deal with uncertain and probable data. Many researchers prioritize these kinds of data. For instance, they study fuzzy data, interval data, probabilistic models etc. In this article, we proposed a method in which the decision making units are uncertain in their inputs and outputs. In the...
full textFUZZY RISK ANALYSIS BASED ON A NEW METHOD FOR RANKING GENERALIZED FUZZY NUMBERS
Fuzzy risk analysis, as a powerful tool to address uncertain information, can provide an appropriate method for risk analysis. However, the previous fuzzy risk analysis methods still have some weaknesses. To overcome the weaknesses of existing fuzzy risk analysis methods, a novel method for ranking generalized fuzzy numbers is proposed for addressing fuzzy risk analysis problems. In the propose...
full textA new parametric method for ranking fuzzy numbers based on positive and negative ideal solutions
full text
A Method for Determining the Importance of Customer Demand Based on Rough Set and DEA
Affected by customers’ lack of experiences and personal preferences, the importance of customer demand as 0 by only using Rough Set method frequently occurs. Existing methods could not determine this importance of indicators, so it is usually deleted. A new method combining Rough Set and Data Envelopment Analysis (DEA) to determine importance of customer demand in Quality Function Deployment (Q...
full textMy Resources
Journal title
volume 8 issue None
pages 75- 98
publication date 2013-10
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023