The insurance financial management information system has accumulated a large amount of data as the insurance financial system has improved and the number of people investing in insurance has increased rapidly. The performance of the insurance agency significantly contributes to the industry’s growth, which leads to economic prosperity. Different financial ratios were developed to investigate it, taking into consideration the insurance provider’s stability, insolvency, profitability, and leverage. The profitability of organizations and insurers is used to evaluate the general effectiveness. In order to achieve this goal, this study examines the impact of insolvency, leverage, stability, scope, and impartiality of capital on the efficiency of Chinese life insurers. The study of financial statements examines a company’s overall financial health throughout time. It is a method of identifying a company’s financial assets and liabilities by integrating a statement of financial position and balance sheet features. It provides a systematic approach to assessing and evaluating the company’s predicament. Using the experimental results, the scores of several insurance firms are compared, and their performance is described based on these results. The effective use of these data to assist decision-makers in developing more reasonable financial insurance investment policies have emerged as a significant challenge that must be addressed. This study utilized the decision tree C 4.5 mining algorithm to analyze insurance financial system data, identify key factors influencing insurance finance, and assist decision-makers in optimizing policy parameters. Finally, the consequence of an increase is analyzed using a previously unseen method to assess the precision of the prediction result.

The Markov process is not only the actuarial basis of pricing of long term care (LTC) insurance but also the fundamental for predicting the future elder population and disabled population. This article aims to summarize how the Markov processes or Semi-Markov processes are used in the Long-term care risk and Long-term care insurance. We also discuss the models based on the time-homogeneous and time-inhomogeneous. Moreover, under the GLM framework, some studies show Tweedie-GLM would give more accurate predictions compared with other GLM models and additive models. However, these models, whether based on the Markov process or Semi-Markov process or GLM, have theoretical advantages due to the natural features, the researchers would quickly build the multi-state models, there are still exits many challenges, and they provoke the researchers into some tries of how to deal with the limitations of data, the development of medical technology, and the longer expectancy of life.