Research On Markov Chains Prediction Model Of Insurance Lapse Or Surrender Probability

In real life,the process or phenomenon of multi-state mechanism conversion often occurs,for example,the insurance company's policy can be transferred from the valid state to the claim termination state,from the valid state to the surrender state,or from the invalid state to the valid state before reaching the expiration date.In this paper,the continuous-time time-homogeneous Markov chain is used to construct a prediction model about the probability of insurance lapse or surrender,to calculate the probability of being in various states at any time,and we give a parameter estimation method.In fact,the state of the insurance policy will have discrete events at a specific time,so the multi-stage Markov chain model is used to characterize this feature.That is,at a specific time when a discrete event occurs,a jump matrix is defined to describe the state transition at the specific time.The model is applied to the study of the lapse or surrender probability of insurance companies,and the model parameters are estimated and calibrated by actual data.Finally,the calibrated Markov chain model predicts the lapse or surrender probability and obtains a good prediction result.