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Balancing accuracy, complexity and interpretability in consumer credit decision making: A C-TOPSIS classification approach

Published on Nov 1, 2013in Knowledge Based Systems 5.10
· DOI :10.1016/j.knosys.2013.08.004
Xiaoqian Zhu7
Estimated H-index: 7
(CAS: Chinese Academy of Sciences),
Jianping LiXiaolei19
Estimated H-index: 19
(CAS: Chinese Academy of Sciences)
+ 2 AuthorsChangzhi Liang5
Estimated H-index: 5
(CAS: Chinese Academy of Sciences)
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Abstract
Accuracy, complexity and interpretability are very important in credit classification. However, most approaches cannot perform well in all the three aspects simultaneously. The objective of this study is to put forward a classification approach named C-TOPSIS that can balance the three aspects well. C-TOPSIS is based on the rationale of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). TOPSIS is famous for reliable evaluation results and quick computing process and it is easy to understand and use. However, it is a ranking approach and three challenges have to be faced for modifying TOPSIS into a classification approach. C-TOPSIS works out three strategies to overcome the challenges and retains the advantages of TOPSIS. So C-TOPSIS is deduced to have reliable classification results, high computational efficiency and ease of use and understanding. Our findings in the experiment verify the advantages of C-TOPSIS. In comparison with 7 popular approaches on 2 widely used UCI credit datasets, C-TOPSIS ranks 2nd in accuracy, 1st in complexity and is in 1st rank in interpretability. Only C-TOPSIS ranks among the top 3 in all the three aspects, which verifies that C-TOPSIS can balance accuracy, complexity and interpretability well.
  • References (43)
  • Citations (21)
Cite
References43
Newest
Lu Han2
Estimated H-index: 2
(THU: Tsinghua University),
Liyan Han2
Estimated H-index: 2
(Beihang University),
Hongwei Zhao2
Estimated H-index: 2
(THU: Tsinghua University)
The most commonly used techniques for credit scoring is logistic regression, and more recent research has proposed that the support vector machine is a more effective method. However, both logistic regression and support vector machine suffers from curse of dimension. In this paper, we introduce a new way to address this problem which is defined as orthogonal dimension reduction. We discuss the related properties of this method in detail and test it against other common statistical approaches-pr...
Published on Feb 1, 2013in Knowledge Based Systems 5.10
You-Shyang Chen12
Estimated H-index: 12
,
Ching-Hsue Cheng35
Estimated H-index: 35
(National Yunlin University of Science and Technology)
Banks are important to national, and even global, economic stability. Banking panics that follow bank insolvency or bankruptcy, especially of large banks, can severely jeopardize economic stability. Therefore, issuers and investors urgently need a credit rating indicator to help identify the financial status and operational competence of banks. A credit rating provides financial entities with an assessment of credit worthiness, investment risk, and default probability. Although numerous models h...
Published on Dec 1, 2012in Expert Systems With Applications 4.29
Majid Behzadian10
Estimated H-index: 10
,
S. Khanmohammadi Otaghsara1
Estimated H-index: 1
(IAU: Islamic Azad University)
+ 1 AuthorsJoshua Ignatius15
Estimated H-index: 15
(Universiti Sains Malaysia)
Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art lit...
Published on Dec 1, 2012in Knowledge Based Systems 5.10
Ling-Jing Kao1
Estimated H-index: 1
(NTUT: National Taipei University of Technology),
Chih-Chou Chiu1
Estimated H-index: 1
(NTUT: National Taipei University of Technology),
Fon-Yu Chiu1
Estimated H-index: 1
(NTUT: National Taipei University of Technology)
A Bayesian latent variable model with classification and regression tree approach is built to overcome three challenges encountered by a bank in credit-granting process. These three challenges include (1) the bank wants to predict the future performance of an applicant accurately; (2) given current information about cardholders' credit usage and repayment behavior, financial institutions would like to determine the optimal credit limit and APR for an applicant; and (3) the bank would like to imp...
Published on Nov 1, 2012in Knowledge Based Systems 5.10
Tsui Chih Wu1
Estimated H-index: 1
(Shih Chien University),
Ming-Fu Hsu6
Estimated H-index: 6
(NCNU: National Chi Nan University)
The sub-prime mortgage crisis of 2007 and the global financial tsunami of 2008 have undermined worldwide economic stability. Consequently, the timely analysis of credit risk has become more essential than ever before. The performance of early risk warning mechanisms may vary according to the criteria used and the underlying environment. This study establishes numerous criteria to assess the performance of classifiers and introduces a multiple criteria decision making method to determine suitable...
Published on Sep 1, 2012in Knowledge Based Systems 5.10
Ching-Chiang Yeh1
Estimated H-index: 1
,
Fengyi Lin3
Estimated H-index: 3
(NTUT: National Taipei University of Technology),
Chih-Yu Hsu1
Estimated H-index: 1
(NTUT: National Taipei University of Technology)
In current credit ratings models, various accounting-based information are usually selected as prediction variables, based on historical information rather than the market's assessment for future. In the study, we propose credit rating prediction model using market-based information as a predictive variable. In the proposed method, Moody's KMV (KMV) is employed as a tool to evaluate the market-based information of each corporation. To verify the proposed method, using the hybrid model, which com...
Published on Aug 1, 2012in Applied Soft Computing 4.87
Jianping LiXiaolei19
Estimated H-index: 19
(CAS: Chinese Academy of Sciences),
Gang Li1
Estimated H-index: 1
(CAS: Chinese Academy of Sciences)
+ 1 AuthorsCheng-Few Lee25
Estimated H-index: 25
(RU: Rutgers University)
Credit risk analysis has long attracted great attention from both academic researchers and practitioners. However, the recent global financial crisis has made the issue even more important because of the need for further enhancement of accuracy of classification of borrowers. In this study an evolution strategy (ES) based adaptive L"q SVM model with Gauss kernel (ES-AL"qG-SVM) is proposed for credit risk analysis. Support vector machine (SVM) is a classification method that has been extensively ...
Published on Jul 1, 2012in Expert Systems With Applications 4.29
Sanja Vukovic1
Estimated H-index: 1
(University of Belgrade),
Boris Delibasic9
Estimated H-index: 9
(University of Belgrade)
+ 1 AuthorsMilija Suknovic6
Estimated H-index: 6
(University of Belgrade)
We propose a case-based reasoning (CBR) model that uses preference theory functions for similarity measurements between cases. As it is hard to select the right preference function for every feature and set the appropriate parameters, a genetic algorithm is used for choosing the right preference functions, or more precisely, for setting the parameters of each preference function, as to set attribute weights. The proposed model is compared to the well-known k-nearest neighbour (k-NN) model based ...
Published on Jun 1, 2012in Expert Systems With Applications 4.29
Akhil Bandhu Hens1
Estimated H-index: 1
(IIT-KGP: Indian Institute of Technology Kharagpur),
Manoj Kumar Tiwari45
Estimated H-index: 45
(IIT-KGP: Indian Institute of Technology Kharagpur)
With the rapid growth of credit industry, credit scoring model has a great significance to issue a credit card to the applicant with a minimum risk. So credit scoring is very important in financial firm like bans etc. With the previous data, a model is established. From that model is decision is taken whether he will be granted for issuing loans, credit cards or he will be rejected. There are several methodologies to construct credit scoring model i.e. neural network model, statistical classific...
Published on Feb 1, 2012in Knowledge Based Systems 5.10
Gang Wang9
Estimated H-index: 9
(Hefei University of Technology),
Jerry Ma2
Estimated H-index: 2
(CityU: City University of Hong Kong)
+ 1 AuthorsKaiquan Xu13
Estimated H-index: 13
(CityU: City University of Hong Kong)
Decision tree (DT) is one of the most popular classification algorithms in data mining and machine learning. However, the performance of DT based credit scoring model is often relatively poorer than other techniques. This is mainly due to two reasons: DT is easily affected by (1) the noise data and (2) the redundant attributes of data under the circumstance of credit scoring. In this study, we propose two dual strategy ensemble trees: RS-Bagging DT and Bagging-RS DT, which are based on two ensem...
Cited By21
Newest
Published on Feb 14, 2019
Lili Yuan1
Estimated H-index: 1
(CAS: Chinese Academy of Sciences),
Jianping LiXiaolei19
Estimated H-index: 19
(CAS: Chinese Academy of Sciences)
+ 2 AuthorsDengsheng Wu10
Estimated H-index: 10
(CAS: Chinese Academy of Sciences)
Meta-syntheses from experts’ judgements and quantitative metrics are two main forms of evaluation. But they both have limitations. This paper constructs a framework for mapping the evaluation results between quantitative metrics and experts’ judgements such that they may be solved. In this way, the weights of metrics in quantitative evaluation are objectively obtained, and the validity of the results can be testified. Weighted average percentile (WAP) is employed to aggregate different experts’ ...
Published on Feb 1, 2019in International Journal of Fuzzy Systems 3.08
Kao-Yi Shen10
Estimated H-index: 10
(CCU: Chinese Culture University),
Hioshi Sakai1
Estimated H-index: 1
(Kyushu Institute of Technology),
Gwo-Hshiung Tzeng65
Estimated H-index: 65
(NTPU: National Taipei University)
In the recent years, various statistical and computational intelligence or machine learning techniques have contributed to the progress of automation or semiautomation for measuring consumer credit scoring in the banking sector. However, most of the Taiwanese commercial banks still rely on seasoned staffs’ judgments on making the final approvals or rejections. To enhance the understanding and transparency of a decision support system (or model) that can assist bank staffs on making their consume...
Published on Nov 1, 2018
Tajul Rosli Razak (University of Nottingham), Jonathan M. Garibaldi34
Estimated H-index: 34
(University of Nottingham)
+ 2 AuthorsDaniele Soria12
Estimated H-index: 12
(University of Westminster)
In recent years, researchers have become increasingly more interested in designing an interpretable Fuzzy Logic System (FLS). Many studies have claimed that reducing the complexity of FLSs can lead to improved model interpretability. That is, reducing the number of rules tends to reduce the complexity of FLSs, thus improving their interpretability. However, none of these studies have considered interpretability and complexity from human perspectives. Since interpretability is of a subjective nat...
Published on Jul 1, 2018
Sait Gül1
Estimated H-index: 1
(Beykent University),
Özgür Kabak12
Estimated H-index: 12
(ITU: Istanbul Technical University),
Y. Ilker Topcu13
Estimated H-index: 13
(ITU: Istanbul Technical University)
Abstract Credit rating is a process for building a classification system for credit lenders to characterize current or potential credit borrowers. By such a process, financial institutions classify borrowers for lending decision by evaluating their financial and/or nonfinancial performances. Recently, use of social media data has emerged an important source of information. Accordingly, social media data can be very useful in evaluating companies' credibility when financial or non-financial asses...
Published on May 1, 2018in International Journal of Intelligent Systems 7.23
Sait Gül1
Estimated H-index: 1
(Beykent University),
Özgür Kabak12
Estimated H-index: 12
(ITU: Istanbul Technical University),
Y. Ilker Topcu13
Estimated H-index: 13
(ITU: Istanbul Technical University)
Credit lenders utilize credit rating approaches to provide a classification system for characterizing credit borrowers. In order to measure the borrowers’ credibility, that is, ability and willingness to repay the debt, there are many financial and non-financial criteria that should be considered. The basic aim of this study is to propose a multiple-criteria credit rating approach that integrates different kinds of information and represents the borrowers’ credibility as a distribution among all...
Published on Jan 10, 2018in Neurocomputing 4.07
Qi Zhang1
Estimated H-index: 1
(CityU: City University of Hong Kong),
Jue Wang9
Estimated H-index: 9
(CAS: Chinese Academy of Sciences)
+ 2 AuthorsJian Ma31
Estimated H-index: 31
(CityU: City University of Hong Kong)
With rapid development of financial services and products, credit risk assessment has recently gained considerable attention in the field of financial risk management. In this paper, an improved credit risk assessment approach is presented. Based on the credit data from China Banking Regulatory Commission (CBRC), a multi-dimensional and multi-level credit risk indicator system is constructed. In particular, we present an improved sequential minimal optimization (SMO) learning algorithm, named fo...
Published on Jan 1, 2018in Computational Intelligence and Neuroscience 2.15
Yajiao Tang2
Estimated H-index: 2
(University of Toyama),
Junkai Ji3
Estimated H-index: 3
(University of Toyama)
+ 3 AuthorsYuki Todo6
Estimated H-index: 6
(Kanazawa University)
Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs) have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN) and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a den...
Published on Oct 1, 2017in Knowledge Based Systems 5.10
Artem Bequé2
Estimated H-index: 2
(Humboldt University of Berlin),
Kristof Coussement11
Estimated H-index: 11
(Lille Catholic University)
+ 1 AuthorsStefan Lessmann14
Estimated H-index: 14
(Humboldt University of Berlin)
Abstract Financial institutions use credit scorecards for risk management. A scorecard is a data-driven model for predicting default probabilities. Scorecard assessment concentrates on how well a scorecard discriminates good and bad risk. Whether predicted and observed default probabilities agree (i.e., calibration) is an equally important yet often overlooked dimension of scorecard performance. Surprisingly, no attempt has been made to systematically explore different calibration methods and th...
Published on Jul 1, 2017in European Journal of Operational Research 3.81
Ting Kuo1
Estimated H-index: 1
(Takming University of Science and Technology)
As a tool for decision analysis, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) attempts to choose alternative that should simultaneously have the closest distance from the positive ideal solution (PIS) and the farthest distance from the negative ideal solution (NIS). Although the ranking index of TOPSIS is reasonable, it contains a flaw. That is, this ranking index is irrespective of the weights of separations of an alternative from the PIS and the NIS. In other wor...
Francisco Louzada13
Estimated H-index: 13
(USP: University of São Paulo),
Anderson Ara (USP: University of São Paulo), Guilherme Fernandes2
Estimated H-index: 2
Abstract The need for controlling and effectively managing credit risk has led financial institutions to excel in improving techniques designed for this purpose, resulting in the development of various quantitative models by financial institutions and consulting companies. Hence, the growing number of academic studies about credit scoring shows a variety of classification methods applied to discriminate good and bad borrowers. This paper, therefore, aims to present a systematic literature review...