A preliminary study on deep learning for predicting social insurance payment behavior

Social insurance plays crucial role for protecting the social functionality. Since different occupation would lead to different level salary and different risk, the social insurance for the people is usually designed by the characteristic of their occupation. In Taiwan, Ministry of Health and Welfare provides health care to citizens and guarantees the basic income of their elderly life. Accordingly, Ministry of Health and Welfare operates and sponsor many types of social insurances such as National Pension Insurance, Labor Pension Insurance, etc. However, due to the Population ageing, many social insurances are going to encounter the crisis of pension bankruptcy. Unfortunately, the traditional actuarial methods are based on many hypotheses to calculate cash flow, which cannot predict individuals' personal payment behavior. Besides, most traditional actuarial methods only can estimate short-term cash flow. As the result, the error of traditional actuarial methods would significantly be increased while the period of prediction target is increased. In order to predicting individuals' personal payment behavior with long-term period, we propose a Deep-Learning framework to build a prediction model. Meanwhile, the Recurrent Neural Network (RNN) architecture is adopted to effectively abstract individuals' payment behavior and precisely predict their future payment behavior with a long-term period. We conduct a comprehensive experimental study based on a real dataset collected through Taiwan's Ministry of Health and Welfare. The results demonstrate a significantly improved accuracy of our Deep Learning approach compared with the existing works.