Statistics for Big Data For Dummies
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The insurance industry couldn't survive without the ability to gather and process substantial quantities of data. In order to determine the appropriate premiums for their policies, insurance companies must be able to analyze the risks that policyholders face and be able to determine the likelihood of these risks actually materializing.

Due to substantial increases in the availability of data and the speed and storage capacity of computers, new opportunities have arisen for the insurance industry to increase profits through the improved modeling of risks, the use of more efficient pricing practices, and the ability to offer more specialized products. Additionally, big data may be used for security purposes, such as detecting and preventing insurance fraud.

A good example of the use of big data in the insurance industry is the growing use of telematics devices in the auto insurance industry.

A telematics device transmits computer data wirelessly. It may be used for many purposes, such as increasing the quality of a product or ensuring the safety of a process. A Global Positioning System (GPS) is an example of a telematics device.

An auto insurance company may install a telematics device in a vehicle, with the resulting data transmitted to the insurance company in real time. The data may include the following details:

  • Speed

  • Number of miles driven

  • Braking patterns

  • Time of day when driving takes place

These data can help an insurance company determine the likelihood of a given driver becoming involved in an accident. The company can use this information to set the premium paid by the individual driver.

The benefit to the driver is that he or she may be eligible for lower premiums if the data show a pattern of safe driving. Another benefit is that the driver will have a better understanding of his or her own driving habits, and will gain knowledge about how to drive more safely.

One of the drawbacks of using telematics devices is the need to process and store significant amounts of data. Another potential issue is that insurance companies may receive telematics data from multiple providers, raising the possibility of data-compatibility issues.

Another huge challenge for insurance companies is identifying and quantifying the most important risk factors from the massive amounts of data being gathered. For example, the insurance company must decide how much each mile driven contributes to the likelihood of an accident. This requires a great deal of sophisticated statistical modeling.

Despite these potential problems, the use of telematics devices in the auto insurance industry is expected to grow rapidly in the next few years as the ability to process the required data continues to improve and public acceptance of the idea grows.

Telematics is currently being used far more widely in commercial auto insurance than for personal auto owners. Fleets of trucks and taxis are good examples. But it is beginning to move into the personal auto space on a voluntary basis. Everybody thinks they are a good driver and wants to get a discount for being one.

But this does raise a larger point about big data and privacy. With all this data floating around, where is the line drawn about what companies and governments can legally know about you? There is no simple answer to that question and in fact it is a constant topic of debate in Congress. Beyond your driving habits, everything from location tracking on your mobile device to which websites you surf is potentially out there to be had. And given people's apparent willingness to sacrifice privacy for convenience, it's worth keeping an eye on what companies are doing with your personal data.

The increased use of telematics devices may also provide additional benefits to society, as the data should make it possible for local authorities to improve the safety of roads and bridges by analyzing the factors that are most likely to contribute to accidents.

About This Article

This article is from the book:

About the book authors:

Alan Anderson, PhD, is a professor of economics and finance at Fordham University and New York University. He's a veteran economist, risk manager, and fixed income analyst.

David Semmelroth is an experienced data analyst, trainer, and statistics instructor who consults on customer databases and database marketing.

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