Insurance Fraud Detection using Spiking Neural Network along with NormAD Algorithm

Abstract

General automobile insurance in recent years, has seen a huge escalation of fraud cases. The requirement of utilizing well organised and coherent technique to check on or determine user those are potential frauds. Thus, the deployment of the NormAD algorithm with less delay to enhance the safety and authorized in the operative process. The paper here describes attribute extrication method and Spiking Neural Network structure to resolve the issue of identification of automobile insurance fraud. The attribute second-level extrication algorithm coined in this paper can efficiently derive key attributes and enhance the identification accuracy of succeeding algorithms. So as to achieve to resolve the issue of unstable simulation allotment in the automobile insurance fraud identification scheme, an exemplary distributed method established on the plan of small unit proportion balance is presented. Formulated on the above techniques of attributes extrication and sample division, a model established on Spiking Neural Network with NormAD Algorithm is proposed. This method utilizes the complete goal of implementation of the Spiking Neural Network model algorithm that rely on Spiking Neuron, and ultimately accomplishes in enhancing the exactness of the detection of Automobile Insurance Fraud.