Support vector machines for credit scoring and discovery of significant features

Bellotti, Tony; Crook, Jonathan

doi:10.1016/j.eswa.2008.01.005

Cited by 258 publications

(131 citation statements)

References 13 publications

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“…Over the last decades, there have been lots of classification models and algorithms applied to analyse credit risk, for example decision tree [2], nearest neighbour K-NN, support vector machine (SVM) and neural network [3][4][5][6][7]. One important goal in credit risk prediction is to build the best classification model for a specific dataset.…”

Section: Introductionmentioning

confidence: 99%

Credit scoring with a feature selection approach based deep learning

Nguyen

2016

MATEC Web of Conferences

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Abstract. In financial risk, credit risk management is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. Deep learning is a powerful classification tool which is currently an active research area and successfully solves classification problems in many domains. Deep Learning provides training stability, generalization, and scalability with big data. Deep Learning is quickly becoming the algorithm of choice for the highest predictive accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, reduce the running time, and improve the accuracy of classifiers. In this study, we constructed a credit scoring model based on deep learning and feature selection to evaluate the applicant's credit score from the applicant's input features. Two public datasets, Australia and German credit ones, have been used to test our method. The experimental results of the real world data showed that the proposed method results in a higher prediction rate than a baseline method for some certain datasets and also shows comparable and sometimes better performance than the feature selection methods widely used in credit scoring.

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Section: Introductionmentioning

confidence: 99%

Credit scoring with a feature selection approach based deep learning

Nguyen

2016

MATEC Web of Conferences

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“…computer vision, digital character recognition, etc). These methods, although not offering the same level of understandability as conventional statistical techniques, have been applied in the credit-scoring space with notable success [15][16][17][18][19][20] . The works of Rosenberg and Gleit [21] , Crook et al [2] , and Yu [22] provide a more comprehensive review of these and other contemporary classification methods.…”

Section: Credit Scoringmentioning

confidence: 99%

Default definition selection for credit scoring

Harris¹

2013

AIR

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In this paper some of the main causes of the recent financial crisis are briefly discussed. Specific attention is paid to the accuracy of credit-scoring models used to assess consumer credit risk. As a result, the optimal default definition selection (ODDS) algorithm is proposed to improve credit-scoring for credit risk assessment. This simple algorithm selects the best default definition for use when building credit scorecards. To assess ODDS, the algorithm was used to select the default definition for the random forest tree algorithm. The resulting classification models were compared to other models built using the unselected default definitions. The results suggest that the models developed using the default definition selected by the ODDS algorithm were statistically superior to the models developed using the unselected default indicators.

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“…Artificial neural networks (ANNs) [5], naive Bayes, logistic regression(LR), recursive partitioning, ANN and sequential minimal optimization (SMO) [6], neural networks (Multilayer feed-forward networks) [7], ANN with standard feed-forward network [8], credit scoring model based on data envelopment analysis (DEA) [9], back propagation ANN [10], link analysis ranking with support vector machine (SVM) [11], SVM [12], integrating non-linear graph-based dimensionality reduction schemes via SVMs [13], Predictive modelling through clustering launched classification and SVMs [14], optimization of k-nearest neighbor (KNN) by GA [15], Evolutionary-based feature selection approaches [16], comparisons between data mining techniques (KNN, LR, discriminant analysis, naive Bayes, ANN and decision trees) [17], SVM [18], intelligent-agent-based fuzzy group decision making model [19], SVMs with direct search for parameters selection [20], SVM [21], decision support system (DSS) using fuzzy TOPSIS [22], neighbourhood rough set and SVM based classifier [23], Bayesian latent variable model with classification regression tree [24], integrating SVM and sampling method in order to computational time reduction for credit scoring [25], use of preference theory functions in case based reasoning model for credit scoring [26], fuzzy probabilistic rough set model [27], using rough set and scatter search met heuristic in feature selection for credit scoring [28], neural networks for credit scoring models in microfinance industry [29].…”

Section: Literature Reviewmentioning

confidence: 99%