How can accelerated underwriting be more accurate with predictive analysis?
The demand for efficiency in the insurance industry is at an all-time high. Customers expect a quick, data-driven service to challenge insurance firms. Due to that, accelerated underwriting has recently become a popular practice, improving customer satisfaction and optimizing resource allocation for insurance companies. Accelerated underwriting allows insurance applicants to obtain insurance coverage without a full medical exam or bloodwork panel.
However, achieving accuracy with limited data remains a big challenge. Minitab’s Predictive Analytics tool provides a robust, data-driven solution to enhance this process.
Analyzing the Approved Profiles Using a Regression Tree
We wanted to use Minitab Statistical Software to learn more about the characteristics of applicants that are most likely to be approved for accelerated underwriting. We used the Regression Trees (CART®) machine learning algorithm in Minitab’s Predictive Analytics module. A Regression Tree uses a decision tree algorithm that works by creating a set of yes/no rules that split the data into partitions based on the predictor settings that best separate the data into similar response values. By using this tool, we were able to:
- Identify the most important variables that affect accelerated underwriting approval.
- Discover combinations of predictor settings that are most likely to lead to a lower or higher rate of accelerated underwriting approval.
- Visualize the findings.
- Create business rules that are easy to understand, use, and apply to their process in real-time.
The CART® decision tree identified the key applicant characteristics (predictors) that influence the decision to approve (=1) or reject (=0) accelerated underwriting.
The Relative Variable Importance Chart displayed the significance of predictors. This chart showed that the insurance company has been focusing on the age the most, then BMI, medical history, and gender.
Predict and Improve!
In Settings, you can input the values for each predictor variable. The Prediction section then provides the Fit value, indicating the likelihood of accelerated underwriting approval. With this predictive model, the insurance company can predict who will be approved or denied for accelerated underwriting.
For example, according to our scenario, a 56-year-old married man with 23 BMI, 120k annual income, history of heart disease, fair health, borderline blood pressure and cholesterol level, and who smokes and consumes moderate amounts of alcohol is 66.7% likely to be rejected from the accelerated underwriting and will be redirected to the traditional underwriting process.
This enables insurance companies to better predict resource allocation and allows for a straightforward conversation with potential customers about the potential to qualify for accelerated underwriting.
Optimize Your Underwriting Process with Minitab
By leveraging Minitab’s powerful Predictive Analytics module, insurance companies can significantly enhance the accuracy of their accelerated underwriting process, boosting both efficiency and customer satisfaction.
Download a Free Trial of Minitab statistical software today.
This blog post was written by Jay Jeon, a summer intern at Minitab in 2024.
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