Abstract
P2P (peer-to-peer) lending is an emerging online service that allows individuals to borrow money from unrelated person without the intervention of traditional financial intermediaries. In these platforms, borrowing limit and interest rate are two of the most notable elements for borrowers, which directly influence their borrowing benefits and costs, respectively. To that end, this paper introduces a BP neural network interval estimation (BPIE) algorithm to predict the borrowers’ borrowing limit and interest rate based on their characteristics and simultaneously develops a new parameter optimization algorithm (GBPO) based on the genetic algorithm and our BP neural network predictive model to optimize them. Using real-world data from http://ppdai.com, the experimental results show that our proposed model achieves a good performance. This research provides a new perspective from borrowers in exploring the P2P lending. The case base and proposed knowledge are the two contributions for FinTech research.
Highlights
Recent research about P2P lending is mainly focused on two aspects. e first one is the empirical research of investor’s behavior in the online loan platform to clarify the impact factors of investor’s risk preference [1] and investor’s choice [2,3,4]. e second aspect is borrowers’ credit scoring and their probability of default [5,6,7,8], which is important to control the risk for P2P lending
We develop a BP neural network interval estimation (BPIE) method inspired by Duan and Xie’s work [11], which obtains two-sided confidence interval for the real estate blessed price based on the BP neural network, to generate one-sided and twosided confidence intervals for both maximum borrowing limit and lowest interest rate
We can find that rating information (RRI) (i.e., Magic-Mirror rating) of borrowers is mainly determined by parameters in dimensions of Identity information (IDI), cation information (CFI), and Historical transaction information (HTI) and in turn affects the borrowers’ Borrowing information (BRI) (AMT and RATi tα/2 σ (RAT) included)
Summary
Recent research about P2P lending is mainly focused on two aspects. e first one is the empirical research of investor’s behavior in the online loan platform to clarify the impact factors of investor’s risk preference [1] and investor’s choice [2,3,4]. e second aspect is borrowers’ credit scoring and their probability of default [5,6,7,8], which is important to control the risk for P2P lending. Before predicting the borrowers’ borrowing limit and interest rate, it is worth mentioned that obtaining a confidence interval of borrowing parameters is more credible and practical than an exact number for both researchers and borrowers. For this reason, we employ the three-layer BP neural network model to fit the complex relationship among different features in P2P lending and to further deduce the required confidence intervals. By taking the advantage of our proposed method, we conduct a systematic analysis in terms of both prediction and optimization for borrowers’ borrowing parameters based on real-world data from http://ppdai.com. This paper provides a different perspective for research effort of the P2P lending system
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