Two credit scoring models based on dual strategy ensemble trees

Wang, Gang; Ma, Jian; Huang, Lihua; Xu, Kai

doi:10.1016/j.knosys.2011.06.020

Cited by 201 publications

(86 citation statements)

References 27 publications

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“…In Antón (2007) Wang, Ma, Huang and Xu (2012) analyze an Australian database and a German one in their study, as well as a Chinese database in Wang et al (2011), and they recognize the wide use of discriminant techniques and logit in previous studies, and they advocate for the use of other techniques less used in the study of credit scoring such as the decision tree showing the usefulness of the latter. Other previous studies such as Zhou, Zhang and Jiang (2008), also utilize Australian and German data, and they approach credit scoring through the decision trees technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit concession through credit scoring: Analysis and application proposal

Salas

Manini

Renart

2017

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Purpose: The study herein develops and tests a credit scoring model which can help financial institutions in assessing credit requests. Design/methodology:The empirical study has the objective of answering two questions:(1) Which ratios better discriminate the companies based on their being solvent or insolvent?and (2) What is the relative importance of these ratios? To do this, several statistical techniques with a multifactorial focus have been used (Multivariate Analysis of Variance, Linear Discriminant Analysis, Logit and Probit Models). Several samples of companies have been used in order to obtain and to test the model. Findings:Through the application of several statistical techniques, the credit scoring model has been proved to be effective in discriminating between good and bad creditors. Research limitations/implications:This study focuses on manufacturing, commercial and services companies of all sizes in Spain; Therefore, the conclusions may differ for other geographical locations.-51-Intangible Capital -http://dx.doi.org/10.3926/ic.903 Practical implications:Because credit is one of the main drivers of growth, a solid credit scoring model can help financial institutions assessing to whom to grant credit and to whom deny it. Social implications:Because of the growing importance of credit for our society and the fear of granting it due to the latest financial turmoil, a solid credit scoring model can strengthen the trust toward the financial institutions assessment's. Originality/value:There is already a stream of literature related to credit scoring. However, this paper focuses on Spanish firms and proves the results of our model based on real data.The application of the model to detect the probability of default in loans is original.

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Section: Literature Reviewmentioning

confidence: 99%

Credit concession through credit scoring: Analysis and application proposal

Salas

Manini

Renart

2017

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“…Conventional statistical techniques including logistic regression have been widely used and compared with non-parametric techniques such as classification and regression tree (CART) in building scoring models (e.g. [7,9,12,13,16,30,39,51,55,58,61] ). Logistic regression deals with a dichotomous dependent variable which distinguishes it from a linear regression model, and makes the assumption that the probability of the dependent variable belonging to any of two different classes relies on the weight of the characteristics attached to it [1,4,5,37,41,48] .…”

Section: Related Studiesmentioning

confidence: 99%

“…Advanced statistical techniques such as neural networks have been widely used in building scoring models ( [1,[4][5][6]9,18,29,38,42,55,56] . Also, by way of comparison between neural networks and other non-parametric techniques such as CART, Davis et al [21] compared CART with Multilayer Perceptron Neural Network for credit card applications, and found comparable results for decision accuracy.…”

Section: Related Studiesmentioning

confidence: 99%

Predicting Creditworthiness in Retail Banking with Limited Scoring Data

et al. 2016

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a b s t r a c tThe preoccupation with modelling credit scoring systems including their relevance to predicting and decision making in the financial sector has been with developed countries, whilst developing countries have been largely neglected. The focus of our investigation is on the Cameroonian banking sector with implications for fellow members of the Banque des Etats de L'Afrique Centrale (BEAC) family which apply the same system. We apply logistic regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN) in building our knowledge-based scoring models. To compare various models' performances, we use ROC curves and Gini coefficients as evaluation criteria and the Kolmogorov-Smirnov curve as a robustness test. The results demonstrate that an improvement in terms of predicting power from 15.69% default cases under the current system, to 7.68% based on the best scoring model, namely CCNN can be achieved. The predictive capabilities of all models are rated as at least very good using the Gini coefficient; and rated excellent using the ROC curve for CCNN. Our robustness test confirmed these results. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies previous occupation, borrower's account functioning, guarantees, other loans and monthly expenses as key variables in the forecasting and decision making processes which are at the heart of overall credit policy.

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“…Two dual strategy ensemble trees are introduced to classify credit scoring in [14]. 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 classificat ion accuracy.…”

Section: Related Workmentioning

confidence: 99%

Bank Customer Credit Scoring by Using Fuzzy Expert System

Bazmara¹,

Donighi²

2014

IJISA

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Abstract-Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts.For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.

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Two credit scoring models based on dual strategy ensemble trees

Cited by 201 publications

References 27 publications

Credit concession through credit scoring: Analysis and application proposal

Credit concession through credit scoring: Analysis and application proposal

Predicting Creditworthiness in Retail Banking with Limited Scoring Data

Bank Customer Credit Scoring by Using Fuzzy Expert System

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