The Influence of Open Data on the P2P Loan Default Prediction.

• Main Authors: Ma, Jo-Ya, 馬若雅
• Other Authors: Yang, Ya-Wei
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/36r7hy

碩士 === 輔仁大學 === 金融與國際企業學系金融碩士班 === 107 === P2P lending industry is gradually emerging in Taiwan. Due to the lack of traditional analytical data, it is necessary to collect new data to assist in the analysis. The open data is relatively easy to collect, and the collection cost is low. The study will test the influence of open data on the p2p loan default prediction. We used the Prosper platform as the research object. During the study period, from 2010 to 2014, the regression prediction model was established using logistic regression and random forest. In addition to the control variables, the explanatory variables are added to the control variable model by public data, and the other part is used as the basis for segmentation data, and the predicted default rate after grouping is compared with the default rate of the parent. From the empirical results, it can be known that the data will be higher than the maternal default rate, with a rate of about 60%, and the predicted default rate can be increased by up to 10%. Therefore, the study believes that the open data variable can improve the existing default forecast.