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Proceedings ArticleDOI

Financial risk modelling in vehicle credit portfolio

13 Nov 2014-pp 1-7
TL;DR: In this article, the analysis of a data set comprising of opulent vehicle credit portfolios characterized by relevant variables is presented, which aims at assessing the risk associated with these portfolios and finally presents a predictive model which highlights the important variables and depicts the combination of those variables that classify a client under defaulter or non-defaulter.
Abstract: Luxury cars are a segment of vehicles which are usually bought by people with a higher purchasing power. Still, majority of people make this luxury investment through vehicle finance services. The people from this segment tend to have a good credit record and thus are granted credit by vehicle finance service providers. Despite the good credit record and high purchasing power, a certain amount of risk is associated with these credit portfolios. This study deals with the analysis of a data set comprising of opulent vehicle credit portfolios characterized by relevant variables. It aims at assessing the risk associated with these portfolios and finally presents a predictive model which highlights the important variables and depicts the combination of those variables that classify a client under defaulter or non-defaulter. The study starts with the use of conventional statistical techniques and subsequently presents machine learning approach using three different decision tree classifiers.
Citations
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01 Jan 2003
TL;DR: In this paper, the authors explored the performance of credit scoring using two commonly discussed data mining techniques-classification and regression tree (CART) and multivariate adaptive regression splines (MARS).
Abstract: Credit scoring has become a very important task as the credit industry has been experiencing severe competition during the past few years. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the relative importance of potential input variables, long training process, and interpretative difficulties have often been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring using two commonly discussed data mining techniques-classification and regression tree (CART) and multivariate adaptive regression splines (MARS). To demonstrate the effectiveness of credit scoring using CART and MARS, credit scoring tasks are performed on one bank credit card data set. As the results reveal, CART and MARS outperform traditional discriminant analysis, logistic regression, neural networks, and support vector machine (SVM) approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks.

23 citations

Journal ArticleDOI
TL;DR: The result shows that support vector machine is the most commonly used classifier for credit scores, and while the system performs well, it does not apply approaches with collateral.
Abstract: Credit operations are indispensable in the organizational development of financial institutions. However, misconduct in these operations occurs, and this can lead to financial loss. These consequences are caused by incorrectly granting credit or incorrectly assigning customer ratings and can compromise a credit portfolio. The result shows that support vector machine is the most commonly used classifier for credit scores, and while the system performs well, it does not apply approaches with collateral. The analysis includes 84 studies in this article to propose using statistical methodology to conduct a meta-analysis to compare the results of classification methods. It shows some cases that consider various probability distributions and also survival data. It also elaborates that collateral is not the first approach for credit scoring. The credit scoring system can then give several starting credit scores according to the classifier the user wants to use.

4 citations

References
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Journal ArticleDOI
TL;DR: Experimental results show that SVM is a promising addition to the existing data mining methods and three strategies to construct the hybrid SVM-based credit scoring models are used.
Abstract: The credit card industry has been growing rapidly recently, and thus huge numbers of consumers' credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer's credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant's credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant's credit score from the applicant's input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods.

766 citations


"Financial risk modelling in vehicle..." refers methods in this paper

  • ...Cheng-Lung Huang et al. (2007) [4] used 307 instances of creditworthy applicants and 383 instances where credit is not creditworthy, 6 nominal, 8 numeric attributes, and 1 class attribute....

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  • ...(2007) [4] used 307 instances of creditworthy applicants and 383 instances where credit is not creditworthy, 6 nominal, 8 numeric attributes, and 1 class attribute....

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Journal ArticleDOI
TL;DR: In this paper, Monte Carlo methods were used to study the performance of the largest root test, two trace-type tests and three determinantal tests in the fixed-effects MANOVA model when certain assumptions are violated.
Abstract: Monte Carlo methods were used to study the performance of the largest-root test, two trace-type tests and three determinantal tests in the fixed-effects MANOVA model when certain assumptions are violated. Results indicated that for protection against nonnormality and heterogeneity of covariance matrices, the largest-root test should be avoided, while the Pillai-Bartlett trace test may be recommended as the most robust of the MANOVA tests, with adequate power to detect true differences in a variety of situations.

458 citations


"Financial risk modelling in vehicle..." refers background in this paper

  • ...The building phase leads to the building of the decision tree by training a sample set with attributes [21]....

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  • ...(Olson, 1974) [21]...

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Journal ArticleDOI
TL;DR: As the results reveal, CART and MARS outperform traditional discriminant analysis, logistic regression, neural networks, and support vector machine (SVM) approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks.

366 citations


"Financial risk modelling in vehicle..." refers methods in this paper

  • ...(2006) [7] in their research work used a data set of credit card data comprising 8000 customers, used variables like good credit, bad credit, gender, age, marriage status, educational level, occupation, job position, annual income, residential status and credit limits on CART, MARS, discriminant analysis, logistics regression, neural networks model, support vector machine....

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  • ...Tian-Shyug Lee et al. (2006) [7] in their research work used a data set of credit card data comprising 8000 customers, used variables like good credit, bad credit, gender, age, marriage status, educational level, occupation, job position, annual income, residential status and credit limits on CART,…...

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Journal ArticleDOI
TL;DR: RS-Bagging DT and Bagging-RS DT can be used as alternative techniques for credit scoring and get the better results than five single classifiers and four popular ensemble classifiers.
Abstract: 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 ensemble strategies: bagging and random subspace, to reduce the influences of the noise data and the redundant attributes of data and to get the relatively higher classification accuracy. Two real world credit datasets are selected to demonstrate the effectiveness and feasibility of proposed methods. Experimental results reveal that single DT gets the lowest average accuracy among five single classifiers, i.e., Logistic Regression Analysis (LRA), Linear Discriminant Analysis (LDA), Multi-layer Perceptron (MLP) and Radial Basis Function Network (RBFN). Moreover, RS-Bagging DT and Bagging-RS DT get the better results than five single classifiers and four popular ensemble classifiers, i.e., Bagging DT, Random Subspace DT, Random Forest and Rotation Forest. The results show that RS-Bagging DT and Bagging-RS DT can be used as alternative techniques for credit scoring.

202 citations


"Financial risk modelling in vehicle..." refers methods in this paper

  • ...Gang Wang et al. (2012) [10] in their research used the Australian and Germen credit datasets....

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  • ...(2012) [10] in their research used the Australian and Germen credit datasets....

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Journal ArticleDOI
TL;DR: This study used clustering techniques to preprocess the input samples with the objective of indicating unrepresentative samples into isolated and inconsistent clusters, and used neural networks to construct the credit scoring model.
Abstract: Unrepresentative data samples are likely to reduce the utility of data classifiers in practical application. This study presents a hybrid mining approach in the design of an effective credit scoring model, based on clustering and neural network techniques. We used clustering techniques to preprocess the input samples with the objective of indicating unrepresentative samples into isolated and inconsistent clusters, and used neural networks to construct the credit scoring model. The clustering stage involved a class-wise classification process. A self-organizing map clustering algorithm was used to automatically determine the number of clusters and the starting points of each cluster. Then, the K-means clustering algorithm was used to generate clusters of samples belonging to new classes and eliminate the unrepresentative samples from each class. In the neural network stage, samples with new class labels were used in the design of the credit scoring model. The proposed method demonstrates by two real world credit data sets that the hybrid mining approach can be used to build effective credit scoring models.

190 citations


"Financial risk modelling in vehicle..." refers methods in this paper

  • ...Nan-Chen Hsieh (2005) [13] in their research used a German credit data set of 1000 loan applicants with 700 samples of creditworthy applicants and 300 samples where credit should not be extended....

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