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.

1. 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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2. Digging out useful information from big chunks of data is a step by step process.
• 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.
• 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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3. 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 low-
income 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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