Risk assessment in social lending via random forests

Malekipirbazari, Milad; Aksakallı, Vural

doi:10.1016/j.eswa.2015.02.001

Cited by 327 publications

(203 citation statements)

References 18 publications

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“…The random forest (RF) model is an ensemble machine learning technique based on a combination of classification or regression methods and statistical learning theory [23]. RF models have been well applied in various fields, such as risk analysis [24], ground water studies [25], remote sensing analysis [26], and flood hazard assessment [27] and especially show advantages in land cover classification [28][29][30][31]. There are two important advantages of RF models.…”

Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of Temperature with the Random Forest Model

Pang

Yue

Zhao

et al. 2017

Advances in Meteorology

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The issues with downscaling the outputs of a global climate model (GCM) to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF) model, which has been shown to be superior for large dataset analysis and variable importance evaluation. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction-National Center for Atmospheric Research reanalysis dataset. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models. Principal component analysis (PCA) and partial correlation analysis (PAR) were used in the predictor selection for the other models for a comprehensive study. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.

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Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of Temperature with the Random Forest Model

Pang

Yue

Zhao

et al. 2017

Advances in Meteorology

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“…Serrano-Cinca (2015) [4] used single factor mean test and survival to analyze 24,449 loan sample data of Lending Club platform from 2008 to 2014, and explained that the default factors were loan purpose, annual income, current housing status, credit records, and liabilities. Malekipirbazari (2015) [5] compared different machine learning methods in order to identify high-quality P2P borrowing customers, the results show that random forecasting (RFS) is significantly superior to FICO scoring and LC grade identification in identifying the best borrowers.…”

Section: Literature Reviewmentioning

confidence: 99%

P2P Network Lending, Loss Given Default and Credit Risks

2018

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Peer-to-peer (P2P) network lending is a new mode of internet finance that still holds credit risk as its main risk. According to the internal rating method of the New Basel Accord, in addition to the probability of default, loss given default is also one of the important indicators of evaluation credit risks. Proceeding from the perspective of loss given default (LGD), this paper conducts an empirical study on the probability distribution of LGDs of P2P as well as its influencing factors with the transaction data of Lending Club. The results show that: (1) the LGDs of P2P loans presents an obvious unimodal distribution, the peak value is relatively high and tends to concentrate with the decrease of the borrower's credit rating, indicating that the distribution of LGDs of P2P lending is similar to that of unsecured bonds; (2) The total asset of the borrower has no significant impact on LGD, the credit rating and the debt-to-income ratio exert a significant negative impact, while the term and amount of the loan produce a relatively strong positive impact. Therefore, when evaluating the borrower's repayment ability, it is required to pay more attention to its assets structure rather than the size of its total assets. When carrying out risk control for the P2P platform, it is necessary to give priority to the control of default rate.

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“…Method Native Bays (NB) [13] Decision Trees (DT) [15] Random Forest (RF) [10] Support Vector Machine (SVM) [6] Random Neural Network (RNN) [1] Adaptive Neuro Fuzzy Inference System (ANFIS) [14] a) Naive Bayes (NB) Bayes (NB) classifier is based on Naive Bayes theorem. It simplifies learning assuming that the features are independent for a given class.…”

Section: Qoe Estimation Used Methodsmentioning

confidence: 99%

A Hierarchical Classification Model of QoE Influence Factors

Amour

Souihi

Hoceini

et al. 2015

Lecture Notes in Computer Science

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Abstract. Quality of Service (QoS) optimization are not sufficient to ensure users needs. That's why, operators are investigating a new concept called Quality of Experience (QoE), to evaluate the real quality perceived by users. This concept becomes more and more important, but still hard to estimate. This estimation can be influenced by a lot of factors called : Quality of Experience Influence Factors (QoE IFs). In this work, we survey and review existing approaches to classify QoE IFs. Then, we present a new modular and extensible classification architecture. Finally, regarding the proposed classification, we evaluate some QoE estimation approaches to highlight the fact that categories do not affect in the same the user perception.

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Risk assessment in social lending via random forests

Cited by 327 publications

References 18 publications

Statistical Downscaling of Temperature with the Random Forest Model

Statistical Downscaling of Temperature with the Random Forest Model

P2P Network Lending, Loss Given Default and Credit Risks

A Hierarchical Classification Model of QoE Influence Factors

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