Enhancing Risk Assessment in Underwriting with Comprehensive Data

This article isn't about the methods and models for accurately predicting risk outcomes. Instead, it's about the types of data you can collect and how they can improve risk assessment. Let's explore this by examining the example of diabetes.

Actuaries and underwriters currently have limited data when it comes to assessing the risk of chronic diseases like diabetes. Typically, they rely on basic factors such as weight, age, and smoking status. This is assuming there's no access to detailed medical histories or blood test results. Based on this limited data, accurately assessing the likelihood of diabetes is quite challenging.

Using just these basic parameters, underwriters can only create a broad and generalized risk profile. While age, weight, and smoking status are indeed important indicators, they fail to capture the complex and multifaceted nature of diabetes risk. This often leads to underwriters either overestimating or underestimating an individual's actual risk, which in turn affects policy pricing and risk mitigation strategies.

Imagine potential clients share more detailed information about their symptoms. We're considering cases where clients have not been diagnosed with diabetes yet. In addition to general demographic information, clients provide the following details:

• Consistent increased appetite
• Occasional cravings for sweets
• Sleepiness after meals

While these symptoms could indicate a range of health issues, from Candida to carbohydrate metabolism disorders, they are still insufficient to reliably predict diabetes. This is because these symptoms are non-specific and can be attributed to a variety of conditions. Therefore, relying solely on these symptoms would still leave significant uncertainty in risk assessment.

However, if we add more specific information such as:

• Family history of diabetes
• Darkened skin in areas like natural folds, elbows, or knuckles
• Persistent thirst
• Frequent urination
• Itching all over the body

With such detailed data, we can more confidently assess the client's prediabetic condition. These additional details provide a clearer picture of the individual's health, allowing for a more accurate risk profile.

The primary issue with limited data is the high level of uncertainty it introduces into the underwriting process. When underwriters lack comprehensive information, they must make assumptions or rely on averages, which can be misleading. For example, two individuals might have similar ages and weights, but vastly different health risks due to their family medical history or lifestyle factors.

This lack of granularity can lead to several problems:

1. Inaccurate Risk Assessment: Without detailed health data, underwriters cannot accurately identify high-risk individuals. This could result in higher claim rates and financial losses for insurers.
2. Unfair Premiums: Customers might be charged premiums that do not accurately reflect their risk. High-risk individuals may be undercharged, while low-risk individuals may be overcharged, leading to dissatisfaction and potential loss of customers.
3. Inefficient Resource Allocation: Insurers may allocate resources inefficiently, either by setting aside too much in reserves due to overestimated risks or by not reserving enough, leading to financial strain when claims exceed expectations.

Detailed health data can significantly enhance the accuracy of risk assessments in several ways:

• Better Pattern Recognition: More granular data allows underwriters to recognize patterns and correlations that are not visible with basic demographic information alone. This improves the predictive power of risk models.
• Improved Tailoring of Policies: With comprehensive health data, insurers can tailor policies more precisely to individual needs, offering personalized coverage options that better match the client's risk profile.
• Enhanced Predictive Models: Additional data points enable the development of more sophisticated predictive models that can account for a wider range of variables, leading to more reliable predictions of health outcomes and associated costs.
• Fairer Pricing: Accurate risk assessment leads to fairer pricing, ensuring that premiums are aligned with the actual risk. This improves customer satisfaction and retention.

Having detailed data about the symptoms and illnesses experienced by customers can significantly enhance predictive and machine learning models used in risk assessment. For predictive modeling, enhanced symptom and illness data refine the ability to forecast healthcare utilization and costs more accurately. This is because more granular data allows for better identification of patterns and correlations between symptoms, illnesses, and healthcare outcomes, leading to more precise predictions of future claims and helping tailor health plans to individual needs.

For machine learning models, including neural networks, decision trees, and ensemble methods, the availability of richer datasets with detailed symptom and illness information improves feature engineering and model training. This enables these models to capture complex patterns and interactions within the data, resulting in higher accuracy and reliability in predicting health events and cost drivers. By integrating comprehensive health data, these models can deliver better performance in identifying and managing risks, ultimately leading to more effective and personalized healthcare solutions.