Data Science In Insurance Sector
It is not surprising that insurance companies actively use data science analysis.
The main purpose of applying data science analytics in the insurance industry is the same as other industries: optimizing marketing strategies, increasing the number of customers, developing business, increasing revenue and reducing costs. Various algorithms are used to realize them in the insurance industry.
Some data science applications in the insurance industry:
Personalized Marketing
Customers are always willing to receive personalized services based on their needs and lifestyles. The insurance industry is not an exception. Insurers face the difficulty of providing digital communication with their customers while meeting these demands.
The platforms collect all possible data to identify the needs of large customers. Then, a hypothesis is generated as to which data will be evaluated. Later, suggestions from the results are developed and these suggestions are matched with the appropriate customers.
Recommendation Engines
Recommendation engines are algorithms applied to present suitable offers for customers. They help influence customers’ daily decisions, choices and preferences.
These algorithms use special filtering systems, demographic data, data from surveys, and some personal informations about the insurance experience to determine customers’ preferences.
Based on this, the engines produce insurance offers designed specifically for specific customers. Thus, the insurance company can avoid offering a car insurance offer to a customer seeking a health insurance offer.
Fraud Detection
Insurance fraud brings huge financial losses to insurance companies every year. Data science platforms and software can detect fraudulent activities using different algorithms For this, the algorithm must have a constant data flow.
Insurance companies often use statistical models for fraud detection. These models are based on past fraudulent activities and apply the sampling method to analyze them. In addition, predictive modeling techniques are applied to analyze and filter fraud samples. Identifying links between suspicious activities helps detect frauds that were not previously noticed.
Price Optimization
Price optimization is a very complex concept. Therefore, various combinations of methods and algorithms are used for price optimization. This procedure; although its application in the insurance sector is controversial, it is being adopted by more and more insurance companies day by day.
The price optimization process takes into account changes based on previous years and company policies and is closely related to the price sensitivity of customers.
In other words, past costs, expenses, demands, risks and profits are reflected in the future. Special algorithms give insurers the opportunity to dynamically adjust quoted premiums.
Price optimization helps increase customer loyalty in long term. However, it ensures profit and income maximization.
Demand Forecasting
Insurance companies can reduce their financial losses by predicting the future.
Insurers use highly complex algorithms for this purpose. The most used methods are; decision tree, random forest, binary logistic regression and support vector machine. Many different variables are analyzed with these algorithms. Thus, an individual customer portfolio is made.
Estimating future demands helps collect not too high and not too low competitive premiums. It also contributes to the development of pricing models.
Customer Segmentation
Customers have different expectations from the insurance industry. The insurance industry applies different techniques to increase the number of customers and develop various marketing strategies. In this context, customer segmentation is a key method.
Algorithms perform customer segmentation based on criterias such as financial status, age, and location. Thus, all customers are divided into groups based on their attitudes, preferences, behaviors and personal informations. This grouping enables the development of relevant attitudes and solutions, especially for specific customers.
As a result, cross-selling policies can be developed and personal services can be tailored for each customer segment.
Lifetime Value Estimation
Customers’ lifetime value (CLV) is a complex phenomenon that represents the value a customer adds to the company. This is the difference between the income to be obtained from the customer and the expenses to be spent.
The lifetime value estimation is usually made using customer behavior data to estimate the customer’s profit to the insurance company. Therefore, behavioral models are widely applied to predict cross-buying and retaining customers. The customer’s monetary value and frequency for a company is crucial for calculating future revenue.
Algorithms gather and process all data to make predictions. This ensures that customers’ behaviors and attitudes are estimated and customer-specific services are maintained. In addition, obtaining customers’ insights can be useful for developing marketing strategies.
Risk Assessment
Implementing risk assessment tools in the insurance industry enables estimation of the risk and minimizes risk to reduce losses. There are two main types of risks: pure and speculative. It is necessary to balance the profitability of the company during the risk assessment process and to avoid both types of risks.
Risk assessment lies in determining the amount of risk and causes of risk. These form the basis of data analysis and calculations. The matrix model of the analysis is widely applied in this area. This model provides a systematic approach to comparable risk informations over time. Algorithms that identify and combine data on changing individual risks are used. Then potential risk groups are evaluated. Thus, the company’s risk is estimated.
