Dynamic weighted ensemble classification for credit scoring using Markov Chain

Feng, Xiaodong; Zhong, Bo; Dong, Yuanxiang; Qiu, Jing

doi:10.1007/s10489-018-1253-8

Cited by 36 publications

(19 citation statements)

References 54 publications

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“…We use standard metrics to analyze the performance of the credit classification models, following [12,[19][20][21][22]28]. The metrics include overall accuracy (ACC), Type I error (T1E), and Type II error (T2E), and are depicted by a confusion matrix, as shown in Table 2.…”

Section: Performance Metricsmentioning

confidence: 99%

“…Outside this repository, Feng et al [12] also examined Chinese credit data, as well as, Li et al [19] and Moula et al [22]. In Latin America, Assef et al and Vieira et al [1,31] analyzed a set of a Brazilian bank, Morales et al [21] explored Peruvian microfinance data.…”

Section: Introductionmentioning

confidence: 99%

“…Consumer spending is one of the main drivers of macroeconomic conditions and systemic risk [ 15 ]. Therefore, the analysis of credit granting to consumers becomes relevant [ 12 , 24 ], since individuals may eventually seek loans to meet their consumption needs. In addition, the credit market size demonstrates its importance, as mentioned by Khandani et al [ 15 ] (above USD $13.63 trillion for Americans in 2008), and Li et al [ 19 ] (more than 12% of Chinese GDP, excluding mortgages in 2017).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning predictivity applied to consumer creditworthiness

2020

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Credit risk evaluation has a relevant role to financial institutions, since lending may result in real and immediate losses. In particular, default prediction is one of the most challenging activities for managing credit risk. This study analyzes the adequacy of borrower’s classification models using a Brazilian bank’s loan database, and exploring machine learning techniques. We develop Support Vector Machine, Decision Trees, Bagging, AdaBoost and Random Forest models, and compare their predictive accuracy with a benchmark based on a Logistic Regression model. Comparisons are analyzed based on usual classification performance metrics. Our results show that Random Forest and Adaboost perform better when compared to other models. Moreover, Support Vector Machine models show poor performance using both linear and nonlinear kernels. Our findings suggest that there are value creating opportunities for banks to improve default prediction models by exploring machine learning techniques.

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Section: Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning predictivity applied to consumer creditworthiness

2020

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“…Many analytical techniques have been proposed to distinguish good loan applications from bad applications. For instance, logistic regression [25], linear discriminate analysis [26], and knearest neighbor (KNN) classifiers [27], classification tree [28][29], markov chain [30][31], survival analysis [32], linear and nonlinear programming [33], neural networks [34][35], Support Vector Machines (SVMs) [36][37][38], genetic methods [39][40][41] and so on. Hybrid approaches include fuzzy systems and neural networks [42], fuzzy systems and support vector machines and neural networks and multivariate adaptive regression spines [43].…”

Section: Loan Evaluation and Decision Makingmentioning

confidence: 99%

Borrower-lender Information Fusion for P2P Lending: A Nonparametric Approach

Guo¹,

Jiang²,

Chen³

et al. 2019

ISI

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As known to us, tremendous efforts have been made to exploit information from borrower's credit for loan evaluation in P2P lending, but seldom researches have explored information from investors and borrowers. To this end, we propose an integrated loan evaluation model that exploits and fuses multi-source information from both the borrower and the investor for improving investment decisions in P2P lending. First, based on the borrower's credit, we build a kernel-based credit risk model to quantitatively evaluate each loan. Second, we build an investor composition model that exploits information from the investor's investment behavior for loan evaluation. Then, based on the above two quantitative models and correlation, we define a multi-kernel weight and develop an integrated loan assessment model that can evaluate a loan with both the return and risk. Furthermore, based on the integrated information loan evaluation model, we formalize the investment decisions in P2P lending as a portfolio optimization problem with boundary constraints to help investor make better investment decisions. To validate the proposed model, we perform extensive experiments on the realworld data from the world's largest P2P lending marketplace. Experimental results reveal that the integrated loan evaluation model can significantly enhance investment performance beyond the existing model in P2P market and other baseline models.

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“…Regarding the topic of our research, it is possible to find research papers where attempts to solve the problem of credit scoring are reported. Various supervised classification models have been used in these investigations; the use of Support Vector Machines [7][8][9], Artificial Neural Networks [10][11][12] and Classifier Ensembles [13][14][15][16], among others [17][18][19], stands out. Some of the experimental comparisons made to determine the performance of the classifiers in terms of credit assignment [20][21][22][23] exhibit, in our opinion, certain problems that prevent generalizing the published results.…”

Section: Introductionmentioning

confidence: 99%

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Rangel-Díaz-de-la-Vega

Villuendas-Rey

Yáñez-Márquez

et al. 2020

Applied Sciences

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In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring. To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring. Then, the performance of four associative classifiers was analyzed. The experiments carried out allowed us to determine which sampling algorithms had the best results, as well as their impact on the associative classifiers evaluated. Accordingly, we determine that the Hybrid Associative Classifier with Translation, the Extended Gamma Associative Classifier and the Naïve Associative Classifier do not improve their performance by using sampling algorithms for credit data balancing. On the other hand, the Smallest Normalized Difference Associative Memory classifier was beneficiated by using oversampling and hybrid algorithms.

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Dynamic weighted ensemble classification for credit scoring using Markov Chain

Cited by 36 publications

References 54 publications

Machine learning predictivity applied to consumer creditworthiness

Machine learning predictivity applied to consumer creditworthiness

Borrower-lender Information Fusion for P2P Lending: A Nonparametric Approach

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

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