According to Willis Towers Watson, more than two-thirds of insurers credit predictive analytics with reducing issues and underwriting expenses, and 60% say the data has helped increase sales and profitability.
That figure is expected to grow significantly over the next year, as the inherent value of predictive analytics in insurance is showing itself in myriad applications.
Predictive analytics tools can now collect data from a variety of sources – both internal and external – to better understand and predict the behavior of insureds. Property and casualty insurance companies are collecting data from telematics, agent interactions, customer interactions, smart homes, and even social media to better understand and manage their relationships, claims, and underwriting.
Using the plethora of data now available, here are 10 ways predictive analytics in P&C insurance will change the game in 2020.
Pricing & Risk Selection
This isn’t exactly a new use for predictive analytics in insurance, but pricing and risk selection will see improvement thanks to better data insights in 2020. Given the increased variety and sophistication of data sources, information collected by insurers will be more actionable.
Why do these data sets help predictive analytics improve pricing and risk selection? Because they are largely comprised of firsthand information. Data and feedback collected from social media, smart devices, and interactions between claims specialists and customers is straight from the source. Data that isn’t harvested through outside channels (such as the typical demographic material used in the past, like criminal records, credit history, etc.) is more direct, and can provide valuable insights for P&C insurers.
But just how much data are insurers collecting from IoT-enabled devices? Some reports estimate it’s approximately 10 MB of data per household, per day, and that figure is expected to increase.
Identifying Customers at Risk of Cancellation
Predictive analytics in P&C insurance is going to help carriers identify many customers who require unique attention – for example, those likely to cancel or lower coverage. More advanced data insights will help insurers identify customers who may be unhappy with their coverage or their carrier.
Having this knowledge in hand will put carriers ahead of the game and allow them to reach out and provide personalized attention to alleviate potential issues. Without predictive analytics, insurers could miss credible warning signs and lose valuable time that could be used to remedy any issues.
Identifying Risk of Fraud
P&C insurance companies are always battling various instances of fraud, and oftentimes aren’t as successful as they would like. The Coalition of Insurance Fraud estimates that $80 billion is lost annually from fraudulent claims in the United States alone. Additionally, fraud makes up 5-10% of claims costs for insurers in the United States and Canada.
Using predictive analytics, carriers can identify and prevent potential fraud before it happens, or retroactively pursue corrective measures. Many insurers turn to social media for signs of fraudulent behavior, using data gathered after a claim is settled to monitor insureds’ online activity for red flags.
Customers are always looking for fast, personalized service. In the P&C insurance industry, that can sometimes present a challenge. But with good predictive analytics systems, carriers will be able to prioritize certain claims to save time, money, and resources – not to mention retain business and increase customer satisfaction.
Predictive analytics tools can anticipate an insured’s needs, alleviating their concerns and improving their relationship with their carrier. It can also contribute to tighter management of budgets by employing forecasted data regarding claims, giving insurers a strategic advantage.
Identifying Outlier Claims
Predictive analytics in insurance can help identify claims that unexpectedly become high-cost losses — often referred to as outlier claims. With proper analytics tools, P&C insurers can review previous claims for similarities – and send alerts to claims specialists – automatically. Advanced notice of potential losses or related complications can help insurers cut down on these outlier claims.
Predictive analytics for outlier claims doesn’t have to come into play only after a claim has been filed, either; insurance companies can also use lessons learned from outlier claim data preemptively to create plans for handling similar claims in the future.
Transforming the Claims Process
With predictive analytics, insurers can use data to determine events, information, or other factors that could affect the outcome of claims. This can streamline the process – which traditionally took weeks and even months – and helps the claims department mitigate risks. This also allows insurers to analyze their claims processes based on historical data and make informed decisions to enhance efficiency.
Insurance companies are always looking for ways to get ahead of their competitors, and there’s no better way to do that than by staying on top of industry trends. Just as using predictive analytics was once a new trend for carriers, predictive analytics tools themselves can help carriers plan new products, customer experiences, and technology solutions to position themselves at the forefront of emerging possibilities.
Identifying Potential Markets
Predictive analytics in insurance can help insurers identify and target potential markets. Data can reveal behavior patterns and common demographics and characteristics, so insurers know where to target their marketing efforts. Since there are 3.2 billion people on social media around the world, these platforms have become increasingly important when it comes to identifying potential markets. It’s also influenced customer service: About 60% of Americans say that social media has made the customer service process easier when it comes to obtaining answers and resolving problems.
Focusing on Customer Loyalty
Brand loyalty is important, no matter the product, and now insurers can use predictive analytics to focus on the history and behavior of loyal customers and anticipate what their needs may be. How important is brand loyalty? About half of customers have left a company for a competitor that better suited their needs. Also, this data can help insurers modify their current process or products based on the information.
Providing a Personalized Experience
Many consumers value a customized experience – even when it comes to shopping for insurance. When insurers have access to predictive analytics in insurance, they can comb through IoT-enabled data to understand the needs, desires, and advice of their customers.
Going forward, more and more insurers will use predictive analytics to help forecast events and gain actionable insights into all aspects of their businesses. Doing so provides a competitive advantage that saves time, money, and resources, while helping carriers more effectively plan for a future defined by change. After all, data is only a strategic asset when you can actually put it to work.