Predictive analytics in the insurance industry

Key takeaways

Advancements in data and technology enable insurance companies to expand their data modeling capabilities far beyond the traditional ways data has been used in insurance operations—and with far greater speed and precision.

As new capabilities continue to refine how the insurance industry does business, this article examines some practical ways predictive analytics in insurance is used today to enhance customer relations, improve operational efficiencies, and accelerate product innovation.

What is predictive analytics in insurance?

Predictive analytics refers to the use of historical data, statistical modeling, data mining and machine learning to identify patterns and predict future outcomes.¹ In insurance, predictive analytics is used to strengthen relationships with customers and agents, simplify processes and identify potential business risks. The data used in predictive analytics comes from various sources. 

Using data to forecast insurance risks may not seem like big news (it’s been a commonplace underwriting practice for decades). However, insurers today can leverage forecasting more meaningfully by analyzing large datasets and harnessing advanced computing technology backed by machine learning and artificial intelligence (AI). 

Use cases of predictive analytics in the insurance industry

While access to enriched data for use in insurance has a host of practical insurance applications, the following highlights some practical use cases specific to predictive analytics: 

1. Manage and streamline insurance claims—By leveraging years of historical claims data, insurers can train predictive models to identify patterns that ultimately improve speed and efficiency in the claims adjudication process. This could involve assigning new claims to the proper area in real-time, evaluating loss details and cost estimate information to support speed to payment and identifying potential efficiency issues throughout the claims process to inform new procedures to resolve them. 

2. Engage more dynamically with customers—From sales to service, providing a good customer experience directly correlates to growth and retention. As such, carriers are using predictive analytics to drive customer engagement in many aspects of the insurance lifecycle. A few potential use cases include: 

• Offering digital information and context for a prospect or insured’s needs during the quoting and binding processes based on the individual customer 

• Routing leads to appropriate agents based on a prospect’s situation 

• Personalizing customer communications, which can help gauge customer satisfaction, referral potential, loyalty, and the like 

Learn more: Nationwide E&S/Specialty Programs partners with Kinetic Insurance to offer positive outcomes across every stage of the workers’ compensation lifecycle.  

3. Predict and price customer risk—Evaluating risk and quoting accurate premiums are essential to prudent insurance operations. With access to new types of data for modeling customer risks, predictive analytics tools enable pricing actuaries to make tailored risk assessments and speed up the process of matching price to risk. Accurate pricing reduces loss ratios, enabling carriers to best deliver on their promises to policyholders. 

Learn more: Nationwide’s telematics offerings. 

4. Improve underwriting processes—Insurers can continually refine the underwriting process using predictive analytics. Access to more meaningful data means underwriters can handle more quoting volume without compromising accuracy. With machine learning capabilities, insurers can leverage data more thoughtfully and learn more quickly.  

5. Detect and prevent fraud—From falsifying or exaggerating claims to manipulating insurance applications, insurance fraud costs the insurance industry billions of dollars annually. To address fraud, emerging tools can be useful in referring claims for further review. Although not conclusive of fraud, these tools can help monitor and manage potential fraudulent activities and can protect insureds from the impact erroneous claims have on the industry. 

The benefits of predictive analytics in insurance

Now that we’ve covered the practical use cases for predictive analytics, let’s examine some significant benefits: 

1. Improved customer experiences and CRM—By analyzing customer preferences, insurance companies and agents alike can use predictive analytics to offer a more personalized experience. Specifically, predictive analytics can be used to determine customer behavior patterns, allowing insurers to better predict the needs of insurance buyers and provide timely service. Further, predictive analytics can be used to identify unhappy customers, giving agencies the opportunity to intervene and potentially prevent lost business. 

2. Better business insight—Advancements in predictive analytics, artificial intelligence, enhanced data, and evolved modeling are enabling more accurate risk assessments and significantly improving loss ratios. This is helping the insurance industry to deliver more meaningful insights to clients on a wide range of topics including their coverage options.  

3. Informed risk prevention—Predictive analytics play a significant role when it comes to loss control and precention, particularly as it relates to severe weather. Specifically, predictive models can be used to analyze past weather patterns and other factors to flag areas that are at high risk of severe weather. This data-driven insight is further strengthened by studies from organizations like the Insurance Institute for Business & Home Safety (IBHS). During a recent visit, Mark Berven, President and COO of Nationwide Property & Casualty, got an inside look at IBHS’s hail research and learned how teams were examining the size, weight and impact energy of hailstones to understand how they affect roofing materials. By combining predictive modeling with empirical research, insurers and builders gain a clearer picture of how improved construction standards can reduce future losses. Nationwide members with auto, home, multi-line, or farm coverage can now sign up for free weather alerts² that notify members when severe weather is approaching — including hail larger than one inch, gusts over 58 mph, and sustained winds above 50 mph.  

4. Affordability—From helping meet a customer’s individual needs or identifying prospective risks, predictive analytics enables insurers to price risk accurately. There is no better example of this than Nationwide’s telematics programs, which use real-world driving data to offer tailored, usage-based insurance that reflects how often and how safely a policyholder drives.  

5. Growth—Predictive analytics gives insurers insight into new markets, where to diversify or fine-tune existing products and services and help identify the most relevant insurance products to attract and serve new clients and stay ahead of the competition. 

6. Increased productivity—Predictive analytics can be used to analyze incoming claims and other data to help work more efficiently. This allows insurance carriers and agencies to more appropriately delegate tasks, ensuring high-priority tasks are completed in a timely fashion.

AI in predictive analytics

In the insurance space, the use of artificial intelligence (AI) has become more common, creating opportunities to better analyze risks and tailor coverage solutions accordingly. Notably, technology helps insurance professionals analyze complex datasets to evaluate risk and enable more precise and personalized pricing. This is already being put into practice when it comes to flood insurance, where modeling data helps with the evaluation of potential property risks. These technologies can also help streamline claims processing and customer engagement strategies. This can lead to faster quoting, reduced claim processing time, automated support, target outreach and more. 

Future of predictive analytics in insurance

As advances in data collection, machine learning and AI continue, predictive analytics is poised for significant growth. Insurance professionals can expect the use of predictive analytics to grow, particularly as it enables them to assess risks more accurately, personalize coverages, and streamline the customer experience. In order to remain competitive, it’s crucial for insurance professionals to have an understanding of predictive analytics and invest in the talent required to leverage it effectively.