An application of locally linear model tree algorithm with combination of feature selection in credit scoring

Siami, Mohammad; Gholamian, Mohammad Reza; Basiri, Javad

doi:10.1080/00207721.2013.767395

Cited by 14 publications

(10 citation statements)

References 41 publications

(60 reference statements)

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“…Hence, the results indicate that the aggregated model outperforms the base classifiers. Moreover, compared to existing fusion techniques, i.e., majority voting [23] and ordered weighting averaging (OWA) [24], the Choquet fuzzy integral performed better.…”

Section: Defined By Equation 9 • Update the Initial Fuzzy Densities Umentioning

confidence: 94%

Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model

Namvar

Naderpour

2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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As one of the main business models in the financial technology field, peer-to-peer (P2P) lending has disrupted traditional financial services by providing an online platform for lending money that has remarkably reduced financial costs. However, the inherent uncertainty in P2P loans can result in huge financial losses for P2P platforms. Therefore, accurate risk prediction is critical to the success of P2P lending platforms. Indeed, even a small improvement in credit risk prediction would be of benefit to P2P lending platforms. This paper proposes an innovative credit risk prediction framework that fuses base classifiers based on a Choquet fuzzy integral. Choquet integral fusion improves creditworthiness evaluations by synthesizing the prediction results of multiple classifiers and finding the largest consistency between outcomes among conflicting and consistent results. The proposed model was validated through experimental analysis on a realworld dataset from a well-known P2P lending marketplace. The empirical results indicate that the combination of multiple classifiers based on fuzzy Choquet integrals outperforms the best base classifiers used in credit risk prediction to date. In addition, the proposed methodology is superior to some conventional combination techniques. Keywords-Choquet fuzzy integral; fuzzy measure; credit risk prediction; peer-to-peer lendingI.

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Section: Defined By Equation 9 • Update the Initial Fuzzy Densities Umentioning

confidence: 94%

Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model

Namvar

Naderpour

2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…Many authors, e.g. [14,18,21,26,28,27], and [34], use similar variables, such as income, past loans, savings amount, marital status, type of job, and number of dependents to analyze credit risk with machine learning techniques. Notwithstanding, great part of them is also available in the German and Australian credit data.…”

Section: Datamentioning

confidence: 99%

“…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%

“…Lei et al, Shen et al, Yeh and Lien [ 18 , 26 , 34 ] investigated a Taiwanese credit card database, and Twala [ 30 ] analyzed credit operations in United States, Germany and Australia. These two last countries are also studied by Damrongsakmethee and Neagoe, Feng et al, Kamalloo and Saniee Abadeh, Kozodoi et al, Moula et al, Shi et al, Siami et al and Xiao et al [ 9 , 12 , 14 , 16 , 22 , 27 , 28 , 33 ].…”

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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“…Tony Bellotti and Jonathan Crook [22] tested the credit data in their works and found that the SVM was more successful in establishing the credit default classification method. Siami M. and Gholamian M. R. et al [23] proposed the locally linear model tree algorithm to evaluate customer's credit, and tested the accuracy of the predictions on the Australia and Germany credit data sets. With the development of technology, researchers are inclined to use hybrid methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Scoring with AHP and Fuzzy Comprehensive Evaluation Based on Behavioural Data from Weibo Platform

Yao

Chen

2019

Teh. vjesn.

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It is increasingly necessary to evaluate the customers' credit. In the era of big data, Information on the Internet is commonly used to judge the credit worthiness of customers. Some users' credit information is incomplete or unavailable, so credit managers cannot judge the true credit situation of these users. However, with the support of social data especially behavioural data and credit evaluation system, this problem can be effectively solved. This study used Weibo to obtain the behavioural data of Chinese users for credit evaluation. Two methods are used to calculate the credit scores of Weibo users, which are the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods. By analysing social processes and inviting experts to make decisions, we constructed a credit evaluation system to expose users' behavioural characteristics. We found that the three key indexes determining the user's social credit are personal identification, behavioural characteristics and interaction among friends. Then, AHP was used to determine the weight of each index. Finally, a static algorithm was proposed to compute the credit evaluation system of Weibo users using fuzzy comprehensive evaluation methods.

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An application of locally linear model tree algorithm with combination of feature selection in credit scoring

Cited by 14 publications

References 41 publications

Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model

Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model

Machine learning predictivity applied to consumer creditworthiness

Credit Scoring with AHP and Fuzzy Comprehensive Evaluation Based on Behavioural Data from Weibo Platform

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