Feature Engineering for Credit Risk Evaluation in Online P2P Lending

Feature Engineering for Credit Risk Evaluation in Online P2P Lending

Shuxia Wang (Beijing Institute of Petrochemical Technology, Beijing, China), Bin Fu (Peking University, Beijing, China), Hongzhi Liu (Peking University, Beijing, China), Zhengshen Jiang (Peking University, Beijing, China), Zhonghai Wu (Peking University, Beijing, China) and D. Frank Hsu (Fordham University, New York City, NY, USA)
DOI: 10.4018/IJSSCI.2017040101
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The rise of online P2P lending, as a novel economic lending model, brings new opportunities and challenges for the research of credit risk evaluation. This paper aims to mine information from different data sources to improve the performance of credit risk evaluation models. Be-sides the personal financial and demographic data used in traditional models, the authors collect in-formation from (1) text description, (2) social network and (3) macro-economic data. They de-sign methods to extract features from unstructured data. To avoid the curse of dimensionality caused by too many features and identify the key factors in credit risk, the authors remove the irrelevant and redundant features by feature selection. Using the data provided by Prosper.com, one of the biggest P2P lending platforms in the world, they show that: (1) it can achieve better performance, measured by both AUC (area under the receiver operating characteristic curve) and classification accuracy, by fusion of information from different data sources; (2) it requires only ten features from different data sources to get better performance.
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Personal financial data is the main information source of traditional credit risk evaluation models. Puro et al. (2010) studied the relationship between loan amount, interest rate and the funding success. Their experimental results showed that lower interest rates decrease the chances of getting the loan funded, while lower loan amounts increase the chance of funded. Emekter et al. (2015) studied the relation between various financial factors and the default rate. Their results showed that credit grade, debt-to-income ratio, FICO score and revolving line utilization play an important role in loan defaults. Loans with lower credit grade and longer duration as associated with high default rate.

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