Abstract
This paper proposes a P2P lending agency risk evaluation approach based on an improved RL model, which has rarely been studied before. The approach looks on the task of risk evaluation as a kind of text categorization. In order to solve the problem of small-scale data and data imbalance of P2P agencies for text categorization using existed supervised learning methods, this paper adopts the RL model. Unfortunately, existed RL models are usually time-consuming. Hence this paper continues to put forward an improved RL model to dynamically update sample weights so as to speed up the training of the model. Experimental results have shown that our proposed model is less affected by data imbalance and has good performance compared with other models.
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