When I began my career as a financial reporter for the insurance industry, the data analytics process was incredibly time-consuming and messy. We kept all of our data in an information silo and if we wanted to analyze it, we had to do so by hand. What’s worse, we didn’t always have the most recent information at our fingertips. Brokers often had one set of numbers while underwriters had a completely different set.

To get more organized, my company hired an IT firm to handle the entire data analysis process. The firm used a number of proven strategies to improve how we collected and processed our data, including predictive modeling, fraud detection and demographic analysis. These three strategies can help you get the most value from data analytics in the insurance industry, and offer the best service to your customers.

The Importance of Data Analytics in the Insurance Industry

Data analysis is especially important in the insurance industry. That’s because agents, brokers, financial reporters and other insurance professionals have to gather large amounts of data from their policyholders and process every single number effectively. Everything from premiums to risk factors and a customer’s financial standing must be carefully collected and analyzed.

To get the most benefit from all of their data, insurance companies need to implement the most effective data analytics strategies. Those strategies are: 

  1. Use predictive analysis to optimize premium pricing options; 
  2. Collect data that can be used to detect and prevent insurance fraud; and 
  3. Analyze which lines of insurance are most useful to customers.

When you focus on these three methods, you’ll leverage your customers’ data in a meaningful way, make a greater profit, and offer the best possible services.

Use Predictive Analysis to Optimize Premium Pricing

Insurance companies use advanced statistics and predictive data analysis—commonly called the law of large numbers—to determine how much, on average, policyholders should pay based on their histories. However, this method isn’t foolproof. If an insurance broker doesn’t use the most reliable data analytics strategy, then the premium the broker calculates may not be accurate.

For example, a policyholder with a squeaky-clean record or who is in perfect health may be offered a premium that is much higher than it should be. That policyholder may well be dissatisfied with the service. Likewise, a policyholder with a spotty record or hidden risk factors may be offered a premium that’s much lower than it should be. The result is greater financial risk for the company.

By using the most reliable data analytics in the insurance industry, you can overcome these challenges. Experienced IT firms can collect and analyze this data for you, allowing you to make better predictions about your policyholders’ behavior and risk factors. In the past, this level of in-depth data analysis would have taken brokers and financial reporters far too long to calculate themselves. With help from [LINK WHEN LIVE a fully-staffed IT firm], this level of detail is much easier to achieve, resulting in far more accurate predictions.

After you improve your predictive analysis process, you’ll be able to offer your lowest-risk customers the low premiums they desire, helping you nudge out the competition. Meanwhile, your highest-risk policyholders will pay a fair premium based on their histories, shielding your company from significant financial burden.

Detect and Prevent Fraud in Real Time

Fraudulent claims have always been a serious problem in the insurance industry. The reason why this is such a difficult problem to solve is because brokers, actuaries and investigators often lack the information necessary to detect fraud in real time—much less prevent it.

But what if I told you there is a way to not only respond more quickly to suspected cases of fraud, but also to prevent them entirely? Implementing more accurate data analytics in the insurance industry is one of the most effective ways to combat this issue and stop would-be frauds in their tracks.

There are two ways to use data analytics for fraud detection and prevention.

  1. Gathering all pertinent information: IT firms can collect data related to all of your claims and screen them for common fraudulent tactics. The more information you gather, the better chance investigators have to identify the fraudulent claim. [LINK WHEN LIVE Cloud data storage] and powerful analytics software make it easier to identify fraudulent claims that would have gone undetected in the past. 
  2. Predicting when fraud is likely: Modern data analytics software can do more than just identify past cases of fraud—it also makes it harder for people to commit fraud in the first place. The most common auto insurance scheme happens when a policyholder claims damage to a vehicle, but never repairs the damage. Knowing this, you can require policyholders to submit proof of repair before they’re compensated fully for the claim. Predictive analysis helps you identify patterns like this and close notorious loopholes in the policy’s terms. 

As predictive data analysis in the insurance industry becomes more common and extensive, we’ll likely see far fewer cases of fraud in the future. This, in turn, will reduce your company’s financial risk while decreasing the cost of premiums for honest customers.

Analyze Which Lines of Insurance Are Most Needed

Data analysis in the insurance industry can also help brokers create desirable new plans for their customers. Because insurance companies rely so heavily on the law of large numbers, customers often end up paying for services that they’ll never use. For example, a man without children may have a policy that includes dental and medical care for dependents under the age of five. He will never use this service and may feel that he’s wasting money on it.

By carefully analyzing the most common needs of your policyholders based on detailed demographic information, you’ll avoid offering your customers more or fewer services than they’ll actually use. While it isn’t possible to offer a customized plan for every individual, when you improve your data analysis, you’ll get much closer to offering plans that meet almost all of your customers’ needs. . 

The result is that your customers will only pay for the services they are likely to use, which improves their satisfaction. Moreover, your brokers will have an easier time marketing these plans to customers, which could lead to more sales and higher customer retention. It’s a win-win situation for your company and your customers.  

How to Apply Data Analytics Strategies in the Insurance Industry

Now that you know which strategies to focus on, you’ll need an experienced IT firm to help you achieve your goals. Tek Leaders offers comprehensive data analytics for the insurance industry, including detailed consumer analytics, fraud prevention techniques, and predictive modeling. We have worked very closely with some of the top insurance companies in the country to help them provide the best services to their customers and reduce financial risk. Using these three analytics strategies, you too can harness the power of your data.

To learn more about the benefits of data analytics in the insurance industry, contact Tek Leaders today. Our team of experts will help you create an effective business strategy centered around detailed data analysis. Or, if you have more questions about our data analytics services, you can reach us by email directly.

Author: Shashank Reddy Tummala.

Shashank is the COO of Tek Leaders inc.He helps SMB’s to achieve their goals in their journey of Digital Transformation.