Optimizing the Borrowing Limit and Interest Rate in P2P System: From Borrowers' Perspective

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.