Reject inference in credit scoring using Semi-supervised Support Vector Machines

Li, Zhiyong; Tian, Ye; Li, Ke; Zhou, Fanyin; Yang, Wenzhu

doi:10.1016/j.eswa.2017.01.011

Cited by 107 publications

(73 citation statements)

References 33 publications

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“…We see an attempt from Li et al . () that a semisupervised SVM performs better than simple models without inference.…”

Section: Methodsmentioning

confidence: 94%

“…This situation is very much like the RI problem, where both aim to make use of the full data set and to make classification. We see an attempt from Li et al (2017) that a semisupervised SVM performs better than simple models without inference.…”

Section: Methodsmentioning

confidence: 99%

“…Hence, accepted accounts are not selected randomly, and ignoring the missing data would result in sample bias. As a result, most RI methods are focused on the case of MNAR (Li et al, 2017).…”

Section: Missing Data Mechanismsmentioning

confidence: 99%

“…Supervised learning is not feasible in RI as labels are missing in the training process. In contrast, semisupervised learning is expected to be an effective solution, as it can learn experiences from both labelled and unlabelled samples (Li et al, 2017). For instance, in semisupervised transductive SVMs, each rejected applicant can be added in one at a time, and the hyperplane can be adjusted and optimized gradually, so the final results incorporate information pertaining to all the applicants (Maldonado and Paredes, 2010).…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…The SVM is a machine learning classifier that has recently become popular in credit scoring (Bellotti and Crook, 2009;Huang et al, 2007;Li et al, 2017). It separates two classes of points in the high dimensional space via two hyperplanes (Cortes and Vapnik, 1995).…”

Section: Stage 2: the Good-bad Modelmentioning

confidence: 99%

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Inferring the Outcomes of Rejected Loans: An Application of Semisupervised Clustering

et al. 2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Rejection inference aims to reduce sample bias and to improve model performance in credit scoring. We propose a semisupervised clustering approach as a new rejection inference technique. K‐prototype clustering can deal with mixed types of numeric and categorical characteristics, which are common in consumer credit data. We identify homogeneous acceptances and rejections and assign labels to part of the rejections according to the label of acceptances. We test the performance of various rejection inference methods in logit, support vector machine and random‐forests models based on data sets of real consumer loans. The predictions of clustering rejection inference show advantages over other traditional rejection inference methods. Inferring the label of the rejection from semisupervised clustering is found to help to mitigate the sample bias problem and to improve the predictive accuracy.

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“…We see an attempt from Li et al . () that a semisupervised SVM performs better than simple models without inference.…”

Section: Methodsmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Section: Missing Data Mechanismsmentioning

confidence: 99%

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Section: Stage 2: the Good-bad Modelmentioning

confidence: 99%

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Inferring the Outcomes of Rejected Loans: An Application of Semisupervised Clustering

et al. 2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Predicting loan default in peer‐to‐peer lending using narrative data

Xia

et al. 2019

Journal of Forecasting

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Peer-to-peer (P2P) lending is facing severe information asymmetry problems and depends highly on the internal credit scoring system. This paper provides a novel credit scoring model, which forecasts the probability of default for each applicant and guides the lenders' decision-making in P2P lending. The proposal is expected to improve the existing credit scoring models in P2P lending from two aspects, namely the classifier and the usage of narrative data. We utilize an advanced gradient boosting decision tree technique (i.e., CatBoost) to predict default loans. Moreover, a soft information extraction technique based on keyword clustering is developed to compensate for the insufficient hard credit data. Validated on three real-world datasets, the experimental results demonstrate that variables extracted from narrative data are powerful features, and the utilization of narrative data significantly improves the predictability relative to solely using hard information. The results of sensitivity analysis reveal that CatBoost outperforms the industry benchmark under different cluster numbers of extracted soft information; meanwhile a small number of clusters (e.g., three) is preferred for consideration of model performance, computational cost, and comprehensibility. We finally facilitate a discussion on practical implication and explanatory considerations.

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Combining Classifier Ensembles with Fuzzy Clustering to Predict Online Food Sales

Öner

Oner

2019

Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making

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Reject inference in credit scoring using Semi-supervised Support Vector Machines

Cited by 107 publications

References 33 publications

Inferring the Outcomes of Rejected Loans: An Application of Semisupervised Clustering

Inferring the Outcomes of Rejected Loans: An Application of Semisupervised Clustering

Predicting loan default in peer‐to‐peer lending using narrative data

Combining Classifier Ensembles with Fuzzy Clustering to Predict Online Food Sales

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