Predictive analytics in insurance: 5 uses and benefits

Advancements in data availability and computing 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 statistical and analytical capabilities continue to refine how the insurance industry does business, this article examines five 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?

According to the Harvard Business School, “predictive analytics is the use of data to predict future trends and events. It uses historical data to forecast potential scenarios that can help drive strategic decisions.”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).

Five use cases for 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 five practical use cases specific to predictive analytics:

1. 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, predictive analytics can be useful in referring claims for further review. Although not conclusive of fraud, these tools help monitor and manage potential fraudulent activities and protect insureds from the impact erroneous claims have on the industry.
2. Manage and streamline insurance claims—Based on our consumer research on the insurance claims experience, we know that policyholders value both speed and efficiency when filing and managing their 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.
3. Engage more dynamically with customers—From sales to service, providing a good customer experience directly correlates to growth and retention. As such, predictive analytics is being used to drive customer engagement in many aspects of the insurance lifecycle. A few examples of how data and advanced modeling are being used include the following:
   • Streamlining the claims filing and settlement processes based on historical claims data
   • 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 profile
   • Personalizing customer communications based on behavioral data, which can help gauge customer satisfaction, referral potential, loyalty, and the like
4. Predict and price customer risk—Evaluating risk and quoting accurate premiums are essential to profitability. With access to new types of data for modeling customer risks, predictive analytic 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.
5. 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 built-in machine learning capabilities, insurers can leverage data more thoughtfully and learn more quickly.

Traditional vs. predictive analytics pricing methods

Before big data and modern computing, customizing insurance at the individual level was difficult. As a result, fixed coverage plans based on group-level characteristics were the norm in traditional insurance pricing models. Today, insurers can analyze factors dynamically and at a finer grain, including customer behavior (e.g., hard braking while driving), weather-related data, business and market conditions, and more. Predictive analytics enables insurers to offer accurate, personalized and flexible coverage options to win more business and help ensure appropriately priced protection for customers.

The value of predictive analytics in insurance

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

1. Improved customer experiences—By analyzing customer preferences, insurance companies and agents alike can use predictive analytics to offer a more personalized experience.
2. Better business insight—Predictive analytics leads to more meaningful insights, allowing the industry to more agilely define rate changes, underwriting standards and product changes and pinpoint appropriate adjustments.
3. Optimized operations—Through the insurance industry, the use of predictive analytics can help to build automated and streamlined processes
4. Profitability—From helping meet a customer’s individual needs or identifying prospective risks, predictive analytics enables insurers to price risk accurately.
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 new clients and stay ahead of the competition.

Building technology resiliency

From customer relations to operational efficiencies, predictive analytics is refining how the insurance industry drives efficiencies and product innovation. Technology is constantly evolving, especially within the insurance industry. You can build technology resiliency by staying ahead of the curve and enhancing your services by learning about up-and-coming insurance technology.