Investment in predictive analytics by the auto insurance industry is on the rise to gain better customer insight, accurately forecast sales, and remain competitive in the market.
Over two-thirds of insurers say that predictive analytics helped them reduce underwriting expenses, and 60% reports that predictive analytics helped increase sales and profitability.
Before jumping straight to using predictive analytics and data science in insurance, let’s first understand more about predictive analytics.
Table of Contents
What Is Predictive Analytics?
Predictive analytics is an analytical tool to keep track of historical records and data. It helps forecast upcoming events by searching for connections and logic in these records and data sets.
In the insurance industry, the predictive analytics tool helps you to collect data from several internal and external sources to understand and predict the behavior of insureds. Property and casualty insurance companies gather information from telematics, agent and customer interactions, and social media to manage their relationships, claims, and underwriting.
What Type of Data Have Insurance Companies Historically Used When Setting Rates?
The auto insurance industry relies on data analysis to determine individual rates. These insurance companies also look at individual risk and rating factors to determine car insurance rates.
Traditional data, such as demographics, used by insurance companies include:
- Geographical location
- Age
- Gender
- Marital status
- Years of driving experience
- Driving record
- Claims history
- Credit history
- Previous insurance coverage
- Vehicle type
- Vehicle use
- Miles driven annually
- Coverages and deductibles
6 Ways Predictive Analytics Is Used in the Insurance Industry
Investing in predictive analytics helps auto insurance companies discover new opportunities and sustainable channels for growth. It is because AI allows them to optimize pricing, assess risk and fraud claims, and improve customer targeting.
Here are the best ways to use predictive analytics in the insurance industry for the better.
1- Underwriting and Pricing
In underwriting and pricing, predictive models create the opportunity to make data-driven decisions and define rate changes and new products more efficiently.
For underwriting preparation, “What-if” modeling is one of the most effective tools.
The “what-if” modeling allows insurers to prepare for the underwriting workload, produce data for filings, and evaluate the impact of a change on an insurer business.
It is an excellent tool for carriers who control the underwriting, claims, pricing, and overall guidance of the company. With the “what-if” model, they can accurately make the changes.
2- More Accurate Risk Profiling
Predictive analytics helps auto insurers to retain customers, monitor buyer behavior, and forecast customer actions.
Behavioral intelligence can be used to determine new customer risk. Insurers can use BI to compare each user against a similar group of users to find the most likely outcome.
In fact, an insurance company can also predict churn or customers who are likely to leave.
Additionally, with the help of predictive analytics, insurance companies can anticipate how a customer will react to bundled packages or discounts.
3- Claims Prediction
More than half of current claims activities will be replaced by automation by 2030.
By deploying predictive analytics tools in the claiming process, companies can anticipate an insured’s needs, thereby alleviating their concerns.
It also helps identify claims that unexpectedly become high-cost losses. With predictive analytics, insurers can determine events, information, or other factors that could potentially affect the outcome of claims. It helps to streamline the process and mitigate risks.
4- Fraud Detection
Insurance companies can cross-reference customer behavior against past customer records by analyzing data collected by behavioral biometrics and behavioral analytics. It helps them to highlight suspicious behavior patterns and fraudulent activity.
With predictive modeling, insurers can predict customers who are more likely to commit fraud even before they file a claim.
AI also keeps track of real-time data and alerts the insurer to evaluate the claim. With AI, insurers can create fraudulent risk scores and remove any anomalies from human error when evaluating fraud claims.
5- Identifying or Predicting New Markets
Predictive analytics helps insurers to get a complete view of their customers and identify and target potential markets.
Insurers can aggregate data from the various touch points used by customers to contact, purchase products, and receive service and support. They can also utilize data to reveal potential customer behavior patterns, demographics, and characteristics, enabling them to target their marketing efforts.
6- Improved Security
Insurance companies are facing new threats from cybercriminals stealing insurance benefits or entitlements.
Fraudsters create identities from stolen personal information. They combine a real social security number with a fake name, date of birth, address, and contact information and then apply for an insurance policy.
They also use social engineering schemes, malware, and remote access tools, to take over insurer or policyholder accounts in digital channels and file false insurance claims.
Using passwords and two-factor authentication is not enough to stop these fraudsters.
Therefore, behavioral biometrics technology is useful to accurately distinguish between fraudsters and legitimate users. Behavioral biometrics recognizes any changes and sends an alert that fraudulent activity is underway.
Final Thoughts
Predictive analytics has become very important for insurers. The best way for auto-insurers to fend off competition and thrive in a competitive market is by using cutting-edge insurance analytics software.
Thus, implementing the right predictive modeling in insurance software will help define and deliver rate changes and new products more efficiently.
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