Domonique Rodgers is an alumnus of NC State and now the President of Family First Life Golden State. In the article below Domonique Rodgers explains what insurance analytics are, how they work, and why consumers should have at least a passing knowledge of this aspect of insurance.
Insurance analytics is the analysis of data by insurance companies in order to make better decisions. The goal of insurance analytics is to help companies better understand their customers, their risks, and their needs. By understanding these things, Domonique Rodgers of NC State says companies can make better decisions about what products to offer, how to price them, and how to market them.
Although insurance analytics is a relatively new field, it is rapidly growing in popularity as more and more companies realize its benefits. In this guide, we’ll briefly explain everything you need to know about the field and how insurance analytics may affect your premiums and coverage.
What is Insurance Analytics?
Domonique Rodgers of NC State explains that insurance analytics are the processes used by insurance companies to analyze customer data and make better business decisions. This data can come from a variety of sources, including claims data, consumer behavior data, and pricing data. Companies then utilize these statistics to:
- Make better decisions about everything from pricing to claims processing
- Better understand their consumers and provide better customer support
- Save money by avoiding costly mistakes
To help the reader better understand the dynamics and range of insurance analytics, Domonique Rodgers briefly expanded on the main applications currently used to improve insurance companies.
Risk Analytics
A number of insurance companies have started using data analytics to help identify risk and fraud. This has been done in a number of ways according to Domonique Rodgers of NC State, including using data to help identify patterns of behavior that may be indicative of fraud, using data to help identify customer risk profiles, and using data to help identify areas where claims are more likely to occur.
Risk analytics also investigate whether policies are worth the cost of covering an individual. If someone is deemed high-risk, they will likely have to pay a higher premium to offset the risk of a payout. These analytics help insurance companies avoid losses but also increase the price of individual policies.
Predictive Analytics
Domonique Rodgers of NC State explains that predictive analytics is a type of data analysis that uses historical data to make predictions about future events. Predictive analytics can be used to identify trends and patterns in data, and to generate predictions about future events. Predictive analytics can be used to make decisions about how to manage risk, develop marketing strategies, and make operational decisions.
Predictive analytics can be used to identify which customers are most likely to file a claim, and this information can be used to price insurance products accordingly. Domonique Rodgers of NC State says predictive analytics can also be used to identify which customers are most likely to lapse on their policy, and this information can be used to target marketing and retention efforts.
Customer Analytics
Domonique Rodgers of NC State explains that customer analytics is the process of analyzing customer data to understand customer behavior and needs. This information can be used to improve customer service, target marketing efforts, and reduce fraud and waste. When used effectively, this branch of analytics can help insurance companies better understand their customers and make better decisions about how to serve them. In turn, Domonique Rodgers says this can lead to improved customer satisfaction and loyalty, and increased profits.
Some of the ways that insurance companies apply customer analytics include:
- Identifying which customers are most likely to renew their policies
- Understanding which customers are most likely to file a claim
- Determining which customers are most likely to shop around for new policies
- Analyzing customer satisfaction levels
- Improving customer retention rates
- Enhancing the customer experience
By perfecting their analytic models, Domonique Rodgers of NC State says insurance companies are able to provide better services, policies, and prices.
What Tools Do Insurance Companies Use to Run Analytics?
Insurance companies use a variety of tools to run analytics, including data mining, predictive modeling, and statistical analysis. Domonique Rodgers explains that several software programs allow companies to track data and produce comprehensive reports, including:
- Lemonade – An AI claims team that automatically collates statistical data and produces accurate pricing reports based on possible risks.
- Alteryx – Alteryx is one of the largest and more comprehensive analytics products that helps streamline health insurance claims and monitors risk factors and costs.
- V2verify – V2verify is specifically designed to find and track possible cases of fraud. It is built around voice recognition technology that verifies customers whenever they call to fail a claim.
- ForMotiv – ForMotiv is another fraud-prevention system that tracks consumer inputs as they fill out a claim. It’s designed to predict intent and prevent fraud before it even happens.
Thanks to these advanced technologies, insurance companies can provide better services at lower prices without the threat of fraudulent claims or losses.
Concluding Thoughts
Insurance analytics help providers filter out bad apples and identify possible risks. Thanks to these analytics, Domonique Rodgers of NC State says insurers can adjust their rates and terms to reflect the actual risk they are facing. The data collected can be used to improve the customer experience, identify fraud and abuse, and improve the claims process.
