Insurance Customer Authentication Using SVM and Financial Time Series Analysis for Mobile Applications

Insurance industry facilitates the users to access the information easily in their jobs without the repetition of password and remember the multiple passwords. Current technology attracts the insurers in authentication process. The identity authentification processes requires the customers to jump through the many hoops, which construct an unpleasant customer experience. The proposed method reduces the challenges in insurance business data using the classification algorithms using the support vector machine (SVM)for the mobile Applications since the growing trend in mobile apps will make it easy for the users. A seasonal variations and correlation in this financial time series data using statistical methods and ultimately generate trading signals for the insurance data. The feature extraction process increases the user security. The classification process improves different level of user identity. The support vector machine increases the data validation process quickly. Finally the proposed work enhances the user authentication process. The frame work is implemented using the matlabR2014 software and results were simulated for mobile apps.