Insurance data mining is yet another growing application of big data.

There are two trends that any insurance company loves to see: A growing client base and a widening profit margin.

The key to achieving these objectives is in a clear understanding of the company’s consumer population. Data mining for insurance is the most efficient and effective method for getting past the barriers of big data and making logical, organized predictions about consumer risks and needs.

Mining big data is changing the landscape of the insurance industry. Here’s why:

1. Insurance data mining provides increasingly accurate risk assessments

Arguably, the biggest advantage of data mining for insurance is the ability to make more accurate predictions with regards to an insured’s level of risk. A comprehensive profile of an individual’s claims history, lifestyle, behavior patterns, etc. can be compiled and analyzed to reveal any hidden trends. This provides an all-inclusive evaluation of how likely it is that an insured will file claims in the future, how near in future this may occur, and the frequency in which claims may be filed.

Demographical trends can also be analyzed in order to help insurance agents understand what role an individual’s age, gender, region, etc. may play in their overall risk factor. This type of information can help insurers to determine premium rates with more confidence and accuracy, and will help improve overall levels of satisfaction.

Because data mining for insurance will ensure that an individual’s premiums are based on comprehensive facts and accurate trend analyses, rates will be determined more fairly and with a higher degree of precision.

2. Insurance data mining predicts which customers are likely to buy new policies

Another benefit of data mining for insurance is in it’s ability to help insurers spot patterns which reveal the need or likelihood of an insured to buy other insurance policies.

As an example, a customer who displays a trend in frequently being involved in auto accidents may be more inclined to purchase a policy which supplements accidental injuries.

But this is just the tip of the iceberg.

Data mining can be used to dig deep into the “big picture” of who an individual customer is, and pinpoint which types of insurance policies that he or she is most likely to buy. Age, gender, ethnicity, marital status, income levels, spending habits, family size, lifestyle choices - all of these factors and many more can be analyzed and organized into a report that will tell insurers who to approach for the sale or up-sale of insurance policies.

Not only is this good for the insurance business, but it’s also beneficial to the insured. When the insurer is able to target an individual as a strong candidate for “X” insurance, he or she may be able to assist a customer in buying a policy they have been meaning to purchase but were putting off, or introducing a customer to an insurance policy that they would like, but did not previously know existed.

3. Insurance data mining detects fraud

Lastly, but certainly not least, data mining for insurance can end the time and headaches associated with attempting to make distinctions between legitimate and fraudulent insurance claims.

By adding automation to fraud detection, the process becomes significantly less expensive and far more efficient.

Ultimately, data mining transforms fraud-fighting from a reactive mindset to a proactive one, as it is able to sort through and analyze vast databases and pinpoint any repetitions or anomalies consistent with fraud. From here, these suspicious claims can be flagged for deeper review.

In this way, data mining for insurance can help to uncover fraud cases that may have otherwise gone undetected.

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