The Role of Predictive Analytics in Modern Insurance

Learn how insurance companies use predictive analytics to improve their operations. Discover tools to easily implement predictive analytics in your business.

The insurance industry, facing numerous challenges from inflation to natural disasters, has turned to predictive analytics to support its operations on multiple fronts.

Predictive analytics use historical and real-time data to predict future events and outcomes. Technologies such as machine learning and statistical modeling are responsible for generating these predictions and helping insurance providers strengthen their decision-making.

How predictive analytics can be used in insurance

In the insurance industry, predictive analytics is helping identify fraud, assess risk, calculate premiums, and create personalized experiences.

Fraud detection

In the US alone, insurance fraud creates an estimated annual damage of $308.6 billion. At the same time, technology has lowered the barriers to entry for aspiring fraudsters, especially now that insurance products can be purchased online. 

AI, for example, can generate synthetic identities that criminals use to commit insurance fraud. “We’ve made it so much easier to do everything without that physical interaction that now synthetic IDs are building up quicker,” explains Joe Stephenson, Director of Digital Intelligence at medical canvassing company Intertel.

By analyzing behavior data, predictive analytics can identify customers who are more likely to commit fraud. Twilio Engage can do this using a feature called Predictive Traits. This feature can be used to find potential signs of fraud using customer and claims data.

Read this recipe to learn more: Use Twilio Segment to Predict Fraudulent Behavior

Once the tool identifies risky policyholders, you can take appropriate steps to mitigate fraud before it has an impact on your business. 

Risk assessment

Predictive models ingest large volumes of data that allow for a granular risk assessment. It includes historical customer and claims data and new data sources, like IoT devices and other types of sensors. 

Modern vehicles, for instance, collect a lot of data about the driver’s behavior, which is very valuable to an insurance provider.

As a result, some insurers have begun offering usage-based insurance (UBI). It uses driving data (speed, braking, mileage, driving times) to gauge the likelihood of getting into an accident. Good drivers are rewarded with lower premiums, leading to happier customers. Almost two-thirds (64%) of UBI policyholders report being “very” or “extremely satisfied” with the program.

Adjust quote premiums

Insurance providers typically set the cost of their products using actuarial models such as cost-plus pricing. This type of pricing determines the cost of the risk and then factors in overhead costs plus a markup percentage.

But cost plus pricing generalizes the cost of the risk and is unable to account for the unique circumstances of each policyholder that could affect risk.

With predictive analytics, insurers are able to tailor their quotes to each customer instead of resorting to a one-size-fits-all approach. UBI is one example of adjusting premiums using a variety of data points, which makes insurance more affordable to low-risk customers.

Personalized customer experience

In the insurance industry, customers are at the highest risk of churn one year after buying their first policy. With personalization, insurance providers will improve retention – according to Segment research, more than half of customers will make a repeat purchase if they have a personalized experience.

Personalization can take the form of predicting which products a customer is most likely to be interested in using historical data. Insurance provider Toggle used Twilio Segment to gather all of their customer data in one place and then categorize policyholders according to different characteristics, such as previously purchased policies.

Todd Wright, Senior Technologist at Toggle, explains: “We can send emails and newsletters to different customer segments with curated content and offers that they are actually interested in, all based on the insurance they have already purchased. For instance, we send information about keeping pets healthy to our pet insurance customers, and we can promote complementary insurance packages based on a user’s purchase history.”

The results were a 67% increase in sales and a better understanding of what drives customer retention, helping Toggle optimize its approach.

Challenges of implementing predictive analytics

When implementing predictive analytics, insurance providers need to tackle challenges all companies face, regardless of industry. They include data quality issues, privacy and security, and predictive model maintenance.

Data quality

High-quality data is integral to predictive analytics. If your predictive models use inaccurate data, they will produce equally inaccurate results. This will negatively impact decision-making, from wasting resources to eroding the customer experience.

Ensuring data quality in a big data system is challenging due to the sheer volume of data streaming in from numerous sources. Preventing duplicate, inconsistent, and otherwise bad data from impacting your predictive models requires you to automatically validate all data upon ingestion.

Automatic data validation is possible with a tracking plan that standardizes data collection. For example, Twilio Segment’s tool Protocols includes a tracking plan that automatically flags quality issues before they impact analytics. 

To improve the quality of your data, you’ll also need to analyze existing data and look for issues such as inconsistent naming conventions.

Data privacy and protection

Insurance providers handle sensitive data, such as customers’ personally identifiable information (PII). But in the hands of the wrong person, PII could be sold on the dark web and used to steal someone’s identity. Without the right security measures, you could also violate data privacy regulations and end up paying expensive fines.

To prevent such severe consequences, you need to proactively implement technology and procedures that protect data privacy. When working with third-party vendors of predictive analytics tools, inquire about their security measures and certificates, as any vulnerabilities on their end could compromise your data.

For instance, Twilio Segment applies automatic PII masking for all users unless you explicitly give someone access to this data. This means only vetted users can see PII, limiting the window of opportunity for threat actors – both internal and external.

Model maintenance

Predictive models must be maintained to keep up with any changes that could impact their forecasts. Not doing so can be an expensive mistake. 

When real estate marketplace Zillow used a predictive model to estimate the prices of homes they would buy and resell, they lost $25,000 on average for every property sold in the last quarter of 2021. The reason? Zillow’s predictive model couldn’t adjust to the emerging inconsistencies of the real estate market.

You can avoid similar mistakes by periodically measuring the accuracy of models and refreshing them as needed.

Advanced analytics with Segment Predictions

Predictions is a feature of Unify, Twilio Segment’s product that unifies all of your data in real time and creates detailed profiles for each customer. 

The feature can predict the chance that a customer will perform a specific tracked event, such as making a purchase or using a promotional code. You can even forecast their lifetime value. Predictions will be saved to individual profiles so you can segment your audience, personalize their journey, and more.

Plus, out-of-the-box models for marketing and data science use cases allow you to quickly get started with predictive analytics even if you don’t have a lot of technical expertise.

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