The insurance market is known to be one of the most dynamic and less predictable areas of industry. It is connected instantly to risk. It has always, therefore, been dependent on statistics. Nowadays, this dependency has been changed forever by insurtech software. 

Today, for the related risk evaluation, insurance firms have a broader variety of knowledge sources. Big Data technology is used to forecast, track, and evaluate risks and claims in order to establish successful strategies for attracting and retaining customers. There is no question that insurance companies benefit from the application of data science within the domains of their great interest. 

Fraud Detection 

Insurance fraud gives insurance firms massive financial losses every year. Platforms and applications for data science have made it possible to identify fraudulent activity, suspicious connections, and subtle patterns of conduct using multiple techniques. 

A constant flow of data should be fed to the algorithm to make this detection possible. For efficient fraud detection, insurance firms typically use mathematical models. These models are based on prior cases of criminal activity and are tested using a sampling process. Moreover, for the study and filtering of fraud cases, predictive modelling techniques are applied here. Identifying connections between suspicious operations helps to detect fraud networks that have not been found before. 

Price optimization 

The technique of price optimization is a complicated idea. Therefore, various combinations of different methods and algorithms are used. Despite the fact that the use of this technique for insurance is still a contentious issue, more and more insurance firms are implementing this approach. 

This method includes the combination of, and further review of, data not related to expected costs and risk characteristics and data not related to expected losses and expenses. That is, the adjustments in relation to the previous year and strategy are taken into account. Price management is thus closely linked to the price sensitivity of the customers. 

Personalized marketing 

Customers are still able to get tailored services that suit perfectly well with their needs and lifestyle. In this instance, the insurance industry is not an exception. In order to fulfil these demands, insurers face the task of maintaining digital contact with their clients. With the aid of artificial intelligence and advanced analytics use cases in insurance, highly customised and appropriate insurance interactions are ensured, drawing insights from a vast amount of demographic information, desires, engagement, actions, attitude, lifestyle data, interests, hobbies, etc. Consumers prefer to search for customised deals, rules, systems of loyalty, tips, and choices. 

The platforms collect all possible data to identify the requirements of the main customers. After that, the idea is made on what will or will not work. Here comes the turn to create the proposal or choose the right one to suit the particular client, which can be done with the aid of selection and matching mechanisms. 

Customer segmentation 

Modern technologies have taken to a qualitatively new stage the marketing of goods and services. For the insurance sector, different customers appear to have unique requirements. In order to maximise the number of clients and to ensure tailored marketing campaigns, insurance marketing uses different techniques. Customer segmentation is proving to be a crucial approach in this regard. 

The algorithms segment clients according to their financial maturity, age, location, etc. Thus, through spotting coincidences in their mood, interests, actions, or personal information, all clients are categorised into categories. This grouping enables the creation of attitudes and solutions particularly applicable to specific customers. 

Lifetime value prediction 

Customer lifetime value (CLV) is a dynamic phenomenon that reflects the value of a customer to a business in the form of the difference between the profits received and the costs forecast in the entire future relationship with a customer. 

Usually, the CLV prediction is evaluated using customer behaviour data in order to predict the profitability of the customer for the insurer. The behavior-based models are thus commonly applicable to cross-buying and retention forecasting. Recency, a client's monetary value for a company, and frequency are considered to be important variables for estimating potential revenue. To construct the prediction, the algorithms put together and process all the data. This enables the possibility of the actions and attitude of the clients, the maintenance of the policies or their surrender to be expected. In addition, the estimation of the CLV can be helpful for the development of the marketing campaign, as it makes the insights of the customers accessible to you. 

Risk assessment 

In order to minimise risks, the introduction of risk management techniques in the insurance sector guarantees the estimation of risk and reduces it to a minimum. Two key forms of risk exist: pure and theoretical. The risk management process is structured to bring balance to the viability of the company and to prevent any of these forms. 

Risk assessment consists of defining the quantification of risk and the reasons for risk. For data processing and calculations, these are the basis. In this area, the matrix model of analysis is widely applied. This model offers a structured approach, comparable in time, to risk knowledge. It is focused on algorithms that detect and combine individual risk data that differ by design, character, and effect. 

Claims prediction 

Insurance firms are especially interested in predicting the future. The accurate outlook offers the company a chance to minimise financial losses. 

For this reason, insurers employ very complicated methodologies. A decision tree, random forest, binary logistic regression, and a support vector machine are the main models. In this case, a great number of different variables are under study. The algorithms include detecting relationships between statements, applying high dimensionality to reach all stages, detecting missing observations, etc. In this way, the portfolio of the individual client is made. Forecasting the future claims allows us to charge not too high and not too low competitive premiums. It also helps to change the pricing models. This helps the insurance industry to be one step ahead of its rivals. 

Conclusion 

Modern technologies are moving extremely quickly, making their way into different business fields. In this respect, there is no lack of the insurance industry behind the others. The use of statistics in the insurance industry has a long history. Therefore, the fact that insurance companies use data science analytics actively is not surprising.