AI News, Top 10 Data Science Use Cases in Insurance

Top 10 Data Science Use Cases in Insurance

The insurance industry is regarded as one of the most competitive and less predictable business spheres.

Big Data technologies are applied to predict risks and claims, to monitor and to analyze them in order to develop effective strategies for customers attraction and retention.

Data science platforms and software made it possible to detect fraudulent activity, suspicious links, and subtle behavior patterns using multiple techniques.

This process supposes combining the data not related to the expected costs and risk characteristics and the data not related to the expected loss and expenses, and its further analysis.

Highly personalized and relevant insurance experiences are assured with the help of the artificial intelligence and advanced analytics extracting the insights from a vast amount of the demographic data, preferences, interaction, behavior, attitude, lifestyle details, interests, hobbies, etc.

Here comes the turn to develop the suggestion or to choose the proper one to fit the specific customer, which can be achieved with the help of the selection and matching mechanisms.

The personalization of offers, policies, pricing, recommendations, and messages along with a constant loop of communication largely contribute to the rates of the insurance company.

Customers lifetime value (CLV) is a complex phenomenon representing the value of a customer to a company in the form of the difference between the revenues gained and the expenses made projected into the entire future relationship with a customer.

The algorithms, also, include analysis of the data gained from simple questionnaires concerning demographic data and some personal information regarding the insurance experience and the insurance object.

Thus, for example, the insurance company can avoid the ambiguity of the offering car insurance to a customer who is searching for a health insurance proposition.

wide range of data including insurance claims data, membership and provider data, benefits and medical records, customer and case data, internet data, etc.

As a result, the aspects such as costs reduction, quality of care, fraud detection and prevention, and consumers engagement increase may be significantly improved.

Implementation of the risk assessment tools in the insurance industry assures the prediction of risk and limits it to the minimum in order to cut losses.

Under conditions of the highly-competitive insurance market, the insurance companies face the everyday struggle to attract as many customers as possible via multiple channels.

The automated marketing is a key to revealing the insights of the customers` attitude and behavior via initial research, product inquiry, purchases, and claims.

As the main goal of digital marketing is to reach a right person at a right time with a right message, life-event marketing is more about the special occasion in the customers’ lives.

Tracking the customer moving through the life cycle, the insurance companies guarantee themselves a constant flow of clients matching a wide range of their suggestions.

In essence, the aim of applying data science analytics in the insurance is the same as in the other industries — to optimize marketing strategies, to improve the business, to enhance the income, and to reduce costs.

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