The language model views human life as a long sequence of events similar in structure to text, where words are formed into sentences. It uses natural language processing techniques to analyze changes in a person's life, prognoses a new event based on detailed data and the sequence of events that have already happened.
The data from 6 million Danes over several years, including the entire chain of life events: education, work, work hours, place of residence, habits, social status, dynamics of health indicators, etc. were used in the experiment.
The scientists took data from a sample of people aged 35 to 65, half of whom died between 2016 and 2020, and asked AI to prognose who would die. The study's results over a four-year period showed that the model's predictions were accurate 78% of the time. The model allows predicting early death and many personality changes that are not possible with other modern tools.
Accurate mechanisms for predicting the occurrence of insurance event can lead to revision and improvement of underwriting procedures in insurance companies. Underwriting is a risk assessment process that insurance companies conduct before issuing insurance policies.
If insurance companies can accurately predict potential claims, they can more effectively determine the degree of risk and set appropriate premiums or insurance conditions. This may include more precise methods of assessing customer data using more advanced risk analysis algorithms and modern technologies such as machine learning and big data analytics.
As a result, insurance companies can more accurately set the price for insurance, offer more suitable coverage to customers, and reduce the likelihood of claims occurring. This, in turn, can improve the efficiency of insurance companies and elevate their financial performance.
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