Abstract
With the developing network technologies and the formation of complete mobile payment systems, the world is witnessing a growing trend of P2P lending. This research paper analyzes the data from a Chinese P2P lending platform PPDai. Also, the research utilizes the Decision Tree algorithm based on attributes related to customers’ loan features, verification statuses and loan history to predict and determine whether a customer could fulfill his/her repayment on time. Hence, P2P lending platforms could have predictions for future customers concerning whether they are potentially failing to pay back money timely. In this way, P2P lending platforms can prevent some potential losses and discouragements from the less trusted customers. Experimental results from the Decision Tree algorithm demonstrate that: the verification processes play a significant role in identifying customers as trusted or not. Moreover, the results propose some broader implications for the healthy development of the online P2P industry for both borrowers and lenders.
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Wu, C. (2021). Risk Analysis in Online Peer-to-Peer Loaning Based on Machine Learning: A Decision Tree Implementation on PPDai.com. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1282. Springer, Cham. https://doi.org/10.1007/978-3-030-62743-0_25
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DOI: https://doi.org/10.1007/978-3-030-62743-0_25
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