The first auto insurance policy was sold to Dr. Truman Martin of Buffalo, New York, in February 1898 by the Travelers Insurance Company. As someone who appreciates data, what stands out to me is that the first odometer patent for an automobile was issued in 1903, and it wouldn’t be until the 1920s before odometers became a standard feature on most automobiles. In the early days of writing auto insurance policies, the industry didn’t have access to such a simple piece of data.
Fast-forward to today when the insurance industry is one of the most important consumers of data. Whereas previously, insurance companies didn’t even have access to data such as how far a car was driven, now some companies will allow you to install devices or software, which provides them with real-time data about your driving behavior.
To further illustrate how quickly things are changing in the insurance industry, the graph below highlights an increase in the number of Google Scholar articles published on the topic of predictive analytics in insurance over the years.
Unfortunately, it’s easy to feel left behind by the new world of data analytics. Even people who work with data on a regular basis can get a little overwhelmed. In describing the success of implementing a new predictive analytics model, insurance provider Lemonade asserts: “it’s not something that an old-fashioned company could simply adopt and adapt; these tools and techniques are difficult to graft onto a company that wasn’t built with them as a core design principle.1”
Fortunately, as data management and predictive analytics become more valuable, you don’t have to have Lemonade’s models to take advantage of the shift in the industry.
Applying Predictive Analytics in the Real World
Here are some relevant examples of predictive analytics use cases in the insurance industry.
Example 1
Using advanced analytics and third-party data to deliver a quote and bind a policy in minutes, rather than days.2
Example 2
Predicting customer churn, so that the right steps are taken to retain customers.3
Example 3
Predicting risk for life insurance and considering multiple models quickly for best results.4
Minitab’s Predictive Analytics Solutions
Luckily, Minitab developed robust tools you need to make it easier than ever to take advantage of your data.
Consider the following cases:
Minitab Statistical Software ensures the capability to use revolutionary predictive analytics models, like TreeNet® and Random Forests® to provide deeper insights in your data. Whether you want to compare the risk profile for property insurance of two adjoining business parks, or flag an inland marine claim for fraud, these powerful predictive analytics tools can bring greater insights from your data.
Minitab Model Ops empowers you to deploy the models you build in Minitab Statistical Software. That way, with entries into a web form, you can get new predictions from your model, in the blink of an eye. For example, a few entries in a web form can generate a prediction from a powerful model that enables you quote business to a new customer.
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Final Thoughts
Each of these tools is powerful alone, but they’re even more powerful together. Use the tools that you trust from Minitab to make it faster and easier to get the insights you need from your data.
Looking to Further Explore Minitab’s Predictive Analytics Solutions?
Sources:
1: Lemonade: How Our Newest Predictive Model Can Take Us to the Next Level of Precision Underwriting and Pricing
2: McKinsey: How Data and Analytics Are Redefining Excellence in P&C Underwriting
3: LUT University: Predicting the Customer Churn with Machine Learning Methods - CASE: Private Insurance Customer Data
4: Springer Link: Risk Prediction in Life Insurance Industry Using Supervised Learning Algorithms