Applications of Data Analysis in Insurance

Applications of Data Analysis in Insurance

Data mining is widely used in various industries. One industry that is making use of its potential is insurance. Data mining is widely used in insurance sector for fixing rates, to predict customers, and to detect fraud.

Claim Prediction Mining

customer

data

helps

insurance

companies to better predict claims. Suppose a car insurance company wants to predict the probability of car accidents that can happen within a specific period of time.

A

prediction model is created based on the customer information provided at the time of signing the insurance policy. The customer’s personal data, attributes of the car to be insured, history of accidents, and other related aspects are used to create the predictive model.

Looking at past data allows the company to know whether or not past customers had an accident during a certain time period. By segregating past customers into different groups based on the costs of their claims, the company gets a record of the data of a past customer at the start of a year and that customer’s claim class for that year. The prediction model created using this information will reveal customer classes that have a high risk of belonging to a bad claim segment.

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Digging out useful information from big chunks of data is a step by step process.

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Data Capture: The first step is to assimilate the data to be used. Car insurance companies usually look for statistical data to set insurance rates.

Data from past auto accidents with details such as age and gender of the drivers, the type of car that is more prone to accidents and theft, the geographical location and the number of accidents in relation to the population are collected to understand the risk involved in providing insurance.

•

Data Processing: The collected data is subjected to data cleansing, data scrubbing, and transforming to improve the process of discovery. During the process, the scraps in the data are removed and the variables for the mining process are reviewed.

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Data Extraction: The third step will include extraction of collected data and further mining of this assimilated data. Selection of the technique is made on the basis of the application and the type of data available.

Price Optimization A recent report indicates that some insurance companies are using data analysis techniques to fix rates. A survey by Earnix, the software solution provider for pricing analytics and optimization used by insurance and banking organizations, showed that 26 percent of all auto insurance companies and 45 percent of the large insurance

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companies use data mining for price optimization. Moreover, 36 percent of all the companies surveyed said they plan to adopt this strategy in the near future.

However, there are divergent views on data mining for price optimization. Insurers argue that price optimization is simply the way to become more efficient by helping insurers make better decisions in the rate-setting process. On the other hand, consumer advocates say that insurers are using price optimization to take advantage of the fact that some people don't shop for insurance. The insurance companies use data mining software to identify which groups which are more likely to accept a price increase and which groups are likely to shop around for a new policy. Critics point out that this information can be used by insurers to impose higher rates on lowincome customers, who have only fewer market choices because of their place of residence, socioeconomic status, and financial literacy.

Other than fixing price rates and predicting customers, insurance companies utilize data review techniques to detect and prevent fraud. They do so by using previously audited claims to build models that will help them detect potentially fraudulent future claims. This would ensure that adjusters focus on claims most likely to be fraudulent, and make use this information to eliminate fraud and recover money.

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