Predictive analytics in the insurance industry

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. Predictive analytics can even be used to identify outlier claims (i.e., claims that unexpectedly lead to high-cost losses). Specifically, through predictive analytics, outlier claims can be quickly compared to other claims, giving adjusters additional data to review claims quickly and determine the most appropriate course of action.
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 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
3. Predict and price customer risk—Evaluating risk and quoting accurate premiums are essential to prudent financial practices. 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.
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 built-in machine learning capabilities, insurers can leverage data more thoughtfully and learn more quickly.
5. Manage data—Predictive analytics can be used by insurance professionals to manage data more efficiently. In particular, predictive analytics allows individuals to analyze historical data to forecast future trends and outcomes. This allows insurance professionals to set premiums and assess risks more accurately. In some cases, it can even help insurers and agents provide more tailored guidance to their customers.
6. 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 can help monitor and manage potential fraudulent activities and can protect insureds from the impact erroneous claims have on the industry.

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 personalized and flexible coverage options to win more business and help ensure appropriately priced protection for customers.

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

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 policies, 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.

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.