Interpretable Machine Learning Based on Integration of NLP and Psychology in Peer-to-Peer Lending Risk Evaluation

With the rapid development of Peer-to-Peer (P2P) lending in the financial field, abundant data of lending agencies have appeared. P2P agencies also have problems such as absconded with ill-gotten gains and out of business. Therefore, it is urgent to use the interpretable AI in Fintech to evaluate the lending risk effectively. In this paper we use the machine learning and deep learning method to model and analyze the unstructured natural language text of P2P agencies, and we propose an interpretable machine learning method to evaluate the fraud risk of P2P agencies, which enhances the credibility of the AI model. First, this paper explains model behavior based on the psychological interpersonal fraud theory in the field of social science. At the same time, the NLP and influence function in the field of natural science are used to verify that the machine learning model really learns the information of part-of-speech details in the fraud theory, which provides the psychological interpretable support for the model of P2P risk evaluation. In addition, we propose “style vectors” to describe the overall differences between text styles of P2P agencies and understand model behavior. Experiments show that using style vectors and influence functions to describe text style differences is the same as human intuitive perception. This proves that the machine learning model indeed learn the text style difference and use it for risk evaluation, which further shows that the model has a certain machine learning interpretability.

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