Zhima Credit Score in Default Prediction for Personal Loans

Wang, Haomin; Chen, Wei; Da, Fang

doi:10.1016/j.procs.2022.01.188

Cited by 9 publications

(1 citation statement)

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“…Some scholars have proposed a strategy of combining soft computing methods with expert knowledge, which combines subjective and objective methods to avoid subjective errors caused by personal emotions, but also takes into account human factors. Then use machine learning algorithms for classification, and use AUC as a performance evaluation indicator [19]. Some researchers use SMOTE oversampling method to process unbalanced credit data sets, and then use C4.5, random forest, SVM, naive Bayesian, KNN and back-propagation neural network to classify the data, and then use accuracy, AUC, precision, recall, average absolute error to evaluate the classification effect of the classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Reports Classification Based on Semi-Supervised Learning Methods

Feng¹,

Han²,

Chen³

2023

Preprint

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Commercial banks usually classify customers according to their credit reports when making loans. In this study, we put our focus on classifying customers based on their credit reports from the People's Bank of China (PBC). Since there are no target labels of users in the credit report of the People's Bank of China, we put forward the fuzzy clustering method for the initial label, and then Construct ant colony search to optimize intelligent recognition. Finally, this study uses SVM, BP neural network, and random forest to classify users and compare their results. The research re-sults indicate that using ant colony clustering algorithm and random forest for classification is the most effective method with the PBC credit reports.

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Section: Literature Reviewmentioning

confidence: 99%