Application of Big Data Technology in Internet Financial Risk Control.

Big data technique is now a prevalent concentration for specialist and academics due to the development of information technology, cloud computing, and Internet of Things technologies. A fiscal risk controlling model using MSHDS-RS, was innovatively proposed to deal with the current situation of unreasonable design of features in risk controlling technique. This model's innovation is that the model utilizes a normalized sparse approach for optimizing feature fusion after drawing loan customer information sources' hard and soft features, thereby forming integrated features. Then, the feature subset derived from probability sampling is trained as a base classifier, and the results of the base classifier are fused and optimized using evidence reasoning rules. MSHDS-RS's accuracy improvement rate was about 3.0% and 3.6% higher than existing PMB-RS methods', respectively, by observing MSHDS-RS's operating results in different feature sets with soft and integrated feature indicators. Therefore, the proposed optimization fusion method is reliable and feasible. This research contributes to the control of internet financial risks and has certain value in making effective decisions on loan platforms. [ABSTRACT FROM AUTHOR]