Data-driven management and the application of machine learning (ML) Underwriting are modern mega trends that have arisen due to the increasing digitalization of society and the economy. The main driver for the introduction of machine learning in all areas is the need to reduce costs. Insurance is no exception, although in some ways it lags behind the trend leaders.
Since the profit of insurance companies is determined by the cost of insured claims, the use of algorithms to detect anomalies in the data inherent in fraudulent transactions can significantly reduce costs. Thus, the insurance company will be able to reduce the risk of unjustified payments by more than 2 times due to an analytical system based on a few methods, which checks bills before they are paid, selecting cases with signs of deviations from the norms of patient management.
Insurance companies’ clients are also interested in technological innovations, as they meet their needs and facilitate interaction with insurers. This is stated in the Insurance report on the views of the country’s insurance industry on artificial intelligence. In a study by a consulting company, which specializes, among other things, in the implementation of information technologies in business processes, it was predicted that the introduction of artificial intelligence technologies would become one of the main ways for the insurance business to combat low customer loyalty.
Experts predict that machine learning algorithms will be widely implemented in various sectors of the insurance industry, working both to increase efficiency and improve the quality of service. At the moment, in the world practice, the main areas of application of ML are the following areas:
- underwriting- risk assessment
- optimization of tariffs “for the client”
- prevention of severe insured events
- detection and prevention of fraud (anti-fraud).
The task of underwriting is perhaps the most promising for the application of ML technologies and big data analysis. In the process of forming insurance rates, the calculation of the probability of an insured event and the assessment of potential risk are used.