Abstract
The emerging online peer to peer (P2P) lending platforms have only a small number of samples in the early stage, it is thus unable to conduct an efficient credit risk assessment on internet loan applicants. In order to solve the sample shortage issue, a virtual sample generation (VSG) methodology integrating multi-distribution mega-trend-diffusion (MD-MTD) and particle swarm optimization (PSO) algorithm is proposed for internet loan credit risk evaluation with small samples. The empirical results indicate that the proposed VSG methodology can greatly help to improve performance of the internet loan credit risk evaluation with small sample datasets.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
