Credit risk assessment in commercial banks based on fuzzy support vector machines

Credit risk assessment plays an important role in banks credit risk management. The objective of credit assessment is to decide credit ranks, which denote the capacity of enterprises to meet their financial commitments. Traditional "one-versusone" approach has been commonly used in the multi-classification method based on Support Vector Machine (SVM). Since SVM for pattern recognition is based on binary classification, there will be unclassifiable regions when extended to multi-classification problems. Focus on this problem, a new credit risk assessment model based on fuzzy SVM is introduced in this paper that can give a reasonable classification for unclassifiable examples. Experiment results show that the fuzzy SVM method provides a better performance in generalization ability and assessment accuracy than conventional one-versus-one multi-classification approach

Weight-Selected Attribute Bagging for Credit Scoring

Assessment of credit risk is of great importance in financial risk management. In this paper, we propose an improved attribute bagging method, weight-selected attribute bagging (WSAB), to evaluate credit risk. Weights of attributes are first computed using attribute evaluation method such as linear support vector machine (LSVM) and principal component analysis (PCA). Subsets of attributes are then constructed according to weights of attributes. For each of attribute subsets, the larger the weights of the attributes the larger the probabilities by which they are selected into the attribute subset. Next, training samples and test samples are projected onto each attribute subset, respectively. A scoring model is then constructed based on each set of newly produced training samples. Finally, all scoring models are used to vote for test instances. An individual model that only uses selected attributes will be more accurate because of elimination of some of redundant and uninformative attributes. Besides, the way of selecting attributes by probability can also guarantee the diversity of scoring models. Experimental results based on two credit benchmark databases show that the proposed method, WSAB, is outstanding in both prediction accuracy and stability, as compared to analogous methods

Combining B&B-based hybrid feature selection and the imbalance-oriented multiple-classifier ensemble for imbalanced credit risk assessment

An ideal model for credit risk assessment is supposed to select important features and process imbalanced data sets in an effective manner. This paper proposes an integrated method that combines B&B (branch and bound)-based hybrid feature selection (BBHFS) with the imbalanceoriented multiple-classifier ensemble (IOMCE) for imbalanced credit risk assessment and uses the support vector machine (SVM) and the multiple discriminant analysis (MDA) as the base predictor. BBHFS is a hybrid feature selection method that integrates the t-test and B&B with the k-fold crossvalidation method to search for a satisfactory feature subset. The IOMCE divides majority samples into several subsets and then combines them with minority samples to construct several training sets for constructing a multiple-classifier ensemble model. We conduct main experiments using a 1:3 imbalanced corporate credit risk data set with continuous features and extended experiments using a 1:5 imbalanced data set with continuous features and a 1:3 imbalanced data set with discrete and nominal features. We combine no feature selection and five feature selection methods (the pure B&B, the factor analysis, the pure t-test, t-test & correlation analysis, and BBHFS) with single-classifier and the IOMCE to construct SVM and MDA models for an empirical comparison. When all features are continuous, the BBHFS-IOMCE method generally outperforms all the other methods. More specifically, BBHFS provides more stable and satisfactory results than the other feature selection methods, and compared with single-classifier models, IOMCE models can significantly enhance the recognition rate for minority samples while incurring a small reduction in the recognition rate for majority samples and maintaining an acceptable overall accuracy. When the features are almost discrete or nominal, the IOMCE method retains its ability to deal with an imbalanced data set, although the five feature selection methods have no significant advantages over no feature selection. This suggests that BBHFS is effective in retaining useful information when reducing the dimensionality of continuous features and that the BBHFS-IOMCE method is an important tool for imbalanced credit risk assessment

Support Vector Machines for Credit Scoring and discovery of significant features

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default. 1

Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

Operations research in consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance- and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer financ