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Data science in insurance is changing the name of the game as we know it. The insurance industry has always relied heavily on the use of data, but we are now seeing interesting changes in the type of data used and how it is being gathered and utilized. Additionally, the areas within the insurance field where this information is being put to use are also changing rapidly. What is data science and how is it being used in the insurance field today? What are the future implications for data science usage in the insurance industry? The paragraphs below are designed to address these important questions.

What is Data Science?

Data science is a unique and broad inter-disciplinary field that relies heavily on a variety of different skills and subjects. Data science focuses primarily on the ability to gather, compare, analyze, and put to use various forms of data to enhance the field in question. Data scientists must be excellent at a number of skills such as advanced mathematics, computer science, technology, statistics, research, and much more. The field of data science is constantly changing as a result of continual improvements and advancements in technology and the other subjects that this field makes use of. Data scientists are employed in just about every avenue of business, medical, healthcare, insurance, and any other field imaginable because all of the sectors require the use and understanding of important data. For this reason, data science continues to be a popular field of study with practically limitless job opportunities.

Related resource: 30 Best Online Master’s in Data Science Degree Programs 2017

How is Data Science Used in Insurance?

Now that we know what is it, how is data science used in the insurance field today? There are actually a number of ways that data science is changing the face of insurance as we know it. Below are several in-depth explanations of some of the ways the insurance field is changing to incorporate big data.

* Personalized Risk

The same way that data science is being used in personalized medicine, it is also changing the way that risk is assessed in the insurance industry. Of course, insurance is all about inherent or proposed risk. The price than an individual ends up paying for their insurance is dependent on the level of risk they are thought to bring to the insurance company. Data scientists within the insurance industry are now using real-time analytics to create more detailed and accurate assessments of the risk involved with each potential client. The not only impacts the insurance company’s bottom line, but it also improves the experience for the client and provides them a better insurance fit based on their actual risk profile. The customer will be happy that they’re not paying a higher premium based on risk factors that don’t really even apply to them, and the insurance company will experience a greater degree of success and financial freedom.

* Auto Insurance

The niche field of auto insurance has long been a tricky one. Any driver can make a mistake, even those with near perfect long-term driving records. It’s next to impossible to predict which drivers this will happen to and what should be done next. Auto insurance can be determined by either of the two methods outlined below.

– Pay as You Go

Pay as you go auto insurance is based on the amount of driving each insured motorist does per month. It’s assumed that the more driving an individual does, the greater their chances of eventually having an accident that would require the use of their insurance policy. Therefore, the more driving an insured motorist engages in, the higher risk they are considered to be. But is this truly the case? One could argue that the more driving you do, the more practice you get. With this theory, someone who does a lot of driving would actually be deemed lower risk than those who drive very little.

– Pay as You Drive

Pay as you drive methods of calculating insurance risk are based on how well a person is estimated to drive. This would be calculated on their past rate of vehicle accidents, traffic tickets, and other infractions. The problem with this way of estimating risk is that everyone can have a bad day or a freak accident. It doesn’t necessarily mean that they are always a high-risk driver or that they are careless.

Data science is capable of providing a unique marriage between the two avenues of assessing insurance risk, providing a more successful model for the insurance company and improved customer satisfaction.

* Life and Health Insurance

It’s no secret that we live in a technology based world. This is no more evident than in the field of healthcare. While healthcare has always relied heavily on the use of data to assess risk for those applying for insurance, data science can change the type of information gathered and the way it is used to determine risk. Take some of the following avenues as examples.

Credit card transactional data – This type of data could be gathered that lets an insurance company know how often a potential client consumes junk food, eats at a fast food restaurant, or purchases high-risk items such as alcohol and cigarettes.

Body sensor data – This type of data could let an insurance company know more about the specific lifestyle of the potential client. This could include such things as how often they exercise, what their body weight and composition are, and possibly even existing signs of impending illness such as cardiovascular disease.

Social media data – This type of information could be useful in determining the overall lifestyle led by the potential client. How are their relationships impacting their health? Do they appear to have a balanced lifestyle that manages the stresses of life in a proper manner?

* Call Center Improvements

Aside from specific genres relating to the insurance field, call center interactions could also be greatly improved with the use of data science implications. The following three avenues of improving current techniques used in call centers are just one example of how these changes could impact the insurance industry.

Training – Claims data could be combined with client data to improve employee training techniques that would allow for a better performance outcome.

Staff Optimization – Current call patterns could be combined with raw data to optimize the techniques that staff members use to convert calls to sales.

Customer Service – Improving the customer service experience is always a top goal within any sales driven field and insurance is no exception. Detailed analytics on call center data could be combined with useful data pulled from social media and other personal avenues to improve the customer experience, therefore enhancing the insurance company’s bottom line.

* Fraud Prevention

Fraud has always been a huge problem within the insurance industry, and it is one of several areas that drives up the cost for everyone involved. Data science could be used to better detect cases of potential fraud, putting a stop to them before they cost the company millions of dollars. Text analytics, predictive analysis, and behavioral analytics can all potentially combine to reduce the threat of insurance fraud, saving the insurance company a great deal of money. Insurance customers also benefit from this increased fraud protection, as the company has more of an incentive to pass these savings on to their beloved clients in a variety of ways.

Hurdles to Overcome

While the insurance field is continually moving in the direction of being more data focused, there are still a few areas that need to be improved upon to see this come to fruition. For one, the cost of obtaining this sort of data is huge. Small and medium sized insurance companies can have a much harder time affording this type of data usage even though the potential for benefits is much larger than the immediate costs. Smaller insurance companies also have far fewer clients to rely on. This gives them a rather limited view of the field of insurance as a whole and a limited perspective on their client base overall.

Large insurance companies have their own unique issues. While they have the funds to afford to be able to make use of big data, many of them have a massively outdated computer and IT infrastructures that would need to be updated to support such data usage. Replacing an entire IT system in a large business or corporation can easily run into the billions of dollars, and many companies just aren’t up to the task.

In spite of the inherent hurdles that still need to be overcome, it’s obvious that a reliance on technology and big data is here to stay. We will continue to see a shift toward data science and its implications for the insurance industry in the next decade.

Data science is a broad field used by just about every successful business, corporation, or organization today. The ability to gather, analyze, and put to use specialized forms of data is vital to the basic functioning of these types of organizations. However, the need for this type of specialized skill will only increase in future decades as we continue to shift into a more technology-based society. The implications for the future use of data science in almost all fields is larger than most of us can currently fathom. The use of data science in insurance will continue to expand in the coming years, opening up many interesting job opportunities along the way for those who choose to enter this constantly changing field.


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