Research on Rapid Identification of Infringement Risk in Financial Technology Data Transactions

Northeastern University, Liaoning, Shenyang 110167, China

Abstract

Aiming at the problems of low accuracy rate of transaction information mining, high error rate of identification of infringement risk of financial technology data transaction and long identification time in current data transaction infringement risk identification methods, a new rapid identification method of infringement risk in financial technology data transactions is proposed. The entropy of clustering is determined by using coverage density and weighted coverage density to mine the transaction information of financial technology data. The BP algorithm is used to train the T-S fuzzy neural network, and the financial technology data transaction information is input into the trained T-S fuzzy neural network to obtain the quick identification result of the infringement risk of financial technology data transaction. The experimental results show that this method has a high accuracy rate of fintech data transaction information mining, a low error rate of fintech data transaction infringement risk identification, and a short recognition time.