Internet financial risk management and control based on improved rough set algorithm

Abstract

With the development of the Internet, Internet finance in new P2P modes will face a great many difficulties and opportunities; so, relevant risk early-warning models need to be researched and analyzed. The early-warning analysis will not only be helpful for P2P, the new mode, but will also be worth learning by the whole Internet financial industries, and there will be a particular demonstration effect. Deep researches have been made on Internet financial risk precautions mainly through analyzing and researching the risks in the leading P2P online debit and credit model within the scope of Internet finance; therefore, risk factors that influence the development of Internet finance are obtained. Next weighting KNN Internet financial risk management and control algorithm with the variable precision rough set is out forward. Training sets of different categories are divided into positive regions and boundary regions through the upper and lower approximation concept of variable precision rough set, thereby acquiring the affiliation regions of the samples based on the similarity between test samples and the sample center. In this way, the category of samples belonging to the positive region can be directly judged, and that of other regions can be judged through the KNN algorithm based on quantitative weighting. Experimental results have verified the effectiveness of the mentioned algorithm.