Financial Transaction Risk Identification Method Based on Boosting-SVM Algorithm

The development of the network economy has brought a great impact on society. In process online transactions, if transaction risks are not prevented and improved in time, it will directly affect normal social economy. Therefore, is necessary to synchronize subjects objects, at same time control risks, promote rapid e-commerce, which an important problem be solved urgently current transactions. This paper first summarizes characteristics risk performance new transactions identifies controls them according their different characteristics. cybercriminal industry uses illegal means seek benefits continuous improvement big data technology mining makes possible identify consumption process. particular, research identification black industries can only ensure operation merchants reduce loss economic interests but also make experience natural users smoother. Based existing data, this adopts Boosting-SVM model black-produced results show that achieves good recognition results. overall prediction accuracy over 95%, rate high-risk 98%, other indicators 96%. Compared with identification, algorithm increased by 1%, more than 8%. Generally speaking, method provides technical support related certain extent.