Workdiamond2019

A study on predicting loan default based on the random forest algorithm

Lin Zhu · Dafeng Qiu · Daji Ergu · Cai Ying · Kuiyi Liu

187 citations0/0 files storedProcedia Computer Science

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Financial Distress and Bankruptcy Prediction FinTech, Crowdfunding, Digital Finance Imbalanced Data Classification Techniques

Abstract

Recently, with the advance of electronic commerce and big data technology, P2P online lending platforms have brought opportunities to businessmen, but at the same time, they are also faced with the risk of user loan default, which is related to the sustainable and healthy development of platforms. Therefore, based on the Random Forest algorithm, this paper builds a loan default prediction model in view of the real-world user loan data on Lending Club. The SMOTE method is adopted to cope with the problem of imbalance class in the dataset, and then a series of operations such as data cleaning and dimensionality reduction are carried out. The experimental results show that: Random Forest algorithm outperforms than logistic regression, decision tree and other machine learning algorithms in predicting default samples.

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References

2014Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending 2018Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning 2015Risk assessment in social lending via random forests 2015Determinants of Default in P2P Lending

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