Online peer-to-peer lending: A review of the literature

Peer-to-peer (P2P) Lending is a type of financial innovation that offers loans without intermediaries to individuals and companies. In the P2P lending system, there is a risk of default on the loan which causes the company to lose. Many studies have to reduce the risk of default by developing a classification model of prediction of default that focuses on increasing accuracy. However, the big problem with prediction is data imbalance and low performance classification algorithms. The purpose of this study is to improve the accuracy of default risk prediction by balancing the data and combining the stacking model ensemble with the meta-learner. The proposed new model consists of 3 optimization parts, the first is Synthetic Minority Oversampling Technique (SMOTE), the second is the selection of features and the third is stacking ensemble learning. The SMOTE method is used to balance the data, the feature selection LightGBM and stacking ensemble learning (LGBFS-StackingXGBoost) to optimize machine learning accuracy. A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. The model was tested using two datasets, namely the online P2P lending dataset and the lending club loan data analysis dataset. The evaluation results show that LGBFS-StackingXGBoost is the best model for both datasets. In the online P2P lending dataset, it received an accuracy of 99,982% and in the lending club loan data analysis dataset, it received an accuracy of 91,434%. This study shows that the accuracy of the prediction model can be improved using the LGBFS-StackingXGBoost method. 

New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning

Determinants of defaults on P2P lending platforms in China

Online peer-to-peer (P2P) lending has developed dramatically over the last decade in China. But this rapid boom carries potential risks. Investors have incurred incalculable losses due to the recent increase in fraudulent and/or unreliable online P2P platforms. Hence, predicting and identifying potential default risk platforms is crucial at this juncture. To achieve this end, we propose a two-step method which employs a deep learning neural network to extract keywords from investor comments and then utilizes a bidirectional long short-term memory (BiLSTM) based model to predict the default risk of platforms. Experimental results on real-world datasets of about 1000 platforms show that in the keyword extraction phase, our model can better capture semantic features from highly colloquial comment-text and achieve significant improvement over other baselines. Additionally, in the default platform prediction stage, our model achieves an F1 value of 80.34% in identifying potential problem platforms, outperforming four baselines by 23.37%, 5.71%, 8.93%, and 4.98% of improvement and comprehensively verifying the effectiveness of our method. Our study provides an alternative solution for platform default risk prediction issues and validates the effectiveness of investor comments in revealing the risk situation of online lending platforms.

Listening to the investors: A novel framework for online lending default prediction using deep learning neural networks

Exploring the lending business crowdfunding to support SMEs' financing decisions

Most previous literatures focus on the micro level default risk of individual borrowers whereas the platform default risk has not been rigorously studied yet. In this paper, we investigate the factors affecting platform default risk by employing the Chinese online P2P platform data. We find significant evidence that severe competition among platforms can increase risky behaviors of platforms by allowing riskier borrowers into the system. Some of the risk management devices could alleviate the default risk of platforms; however, others are not effective at alleviating the default risks. In addition, we find evidence that macro environment such as stock market condition or increases in speculative investment opportunities plays critical roles to increase the platform default rate. Our study sheds light on the platforms’ default risk issues and verifies key factors that influence their risky behaviors.

Factors affecting platform default risk in online peer-to-peer (P2P) lending business: an empirical study using Chinese online P2P platform data

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Factors affecting platform default risk in online peer-to-peer (P2P) lending business: an empirical study using Chinese online P2P platform data