Enhancing Insurance Underwriting: Behavioral Patterns and Actuarial Tables - Decisimo

Transforming Insurance Underwriting: Behavioral Data Meets Actuarial Insight

Today's insurance underwriting is undergoing a significant shift. By blending behavioral pattern analysis with traditional actuarial methods, insurers are achieving more accurate risk assessments and customizing policy pricing. This article delves into how rule engines are central to this advancement, enabling a more detailed evaluation of individual risks.

Shifting Gears in Insurance Underwriting

Gone are the days when insurance underwriting solely depended on demographic data and static actuarial tables. Now, insurers are embracing data analytics to integrate behavioral insights into their models. This shift allows for a deeper understanding of individual risks, moving beyond broad demographic categories to more personalized risk assessments.

Understanding Behavioral Pattern Recognition

Behavioral pattern recognition analyzes individual behaviors and lifestyle choices, offering a deeper insight into risk factors. This approach is particularly effective when combined with rule engines that can process and interpret complex datasets.

Implementing Rule Engines in Underwriting

Integrating Behavioral Data:

• Data Collection: Gather detailed data from telematics in auto insurance (e.g., average speed, frequency of hard braking, driving times) and data from health trackers in life insurance (e.g., daily step count, heart rate variability).
• Rule Engine Configuration: Set up rule engines to analyze this data for risk pattern identification. Below are examples of specific rules:
• Rule 1: Auto Insurance Speeding Pattern: "If average driving speed > speed limit by 20% on more than 50% of trips, then increase risk score by 15%."
• Rule 2: Health Insurance Activity Level: "If daily steps < 3000 for over 60% of the recorded period, then increase health risk score by 10%."
• Rule 3: Heart Rate Variability and Stress: "If heart rate variability < 40ms for over 70% of the recorded period, classify as 'high stress' and increase health risk score by 20%."
• These rules demonstrate how specific behavioral data points can be translated into risk assessment metrics within rule engines.

Enhancing Actuarial Tables with Real-Time Data:

• Use rule engines to update actuarial tables in real-time based on the latest behavioral data, leading to more accurate and current risk assessments.
• Automate the calculation of personalized premiums based on the individual risk profile derived from behavioral patterns.

Case Studies

Auto Insurance: Telematics and Risk Assessment

This case study demonstrates how a rule engine, equipped with a scorecard system, processes and evaluates data from telematics devices to adjust insurance premiums based on driving behavior:

• Telematics Data Collection: Gather data from telematics devices installed in vehicles, which includes metrics like average speed, braking patterns, and duration of driving sessions.
• Scorecard Implementation in Rule Engine: Develop a scorecard within the rule engine where each driving behavior is assigned a score based on its risk level. For example:
• "Speeding (driving over the speed limit by 20% or more) = 10 points"
• "Hard Braking (more than three instances in a trip) = 5 points"
• "Night Driving (driving between 12 AM and 4 AM) = 8 points"
• Calculating Risk Profile: The rule engine calculates a total risk score for each driver based on the accumulated points over a specified period.
• Premium Adjustment: Premiums are adjusted according to the risk score. For instance, a higher total score, indicating riskier driving behavior, results in higher premiums.
• Feedback Loop for Drivers: Drivers receive feedback on their driving habits along with suggestions for improvement, incentivizing safer driving behavior.

Driving Behavior	Criteria	Points
Speeding	10-20% over speed limit	5
	20-30% over speed limit	10
	30%+ over speed limit	15
Hard Braking	1-2 instances in a trip	3
	3-4 instances in a trip	5
	5+ instances in a trip	8
Night Driving	Driving between 12 AM - 4 AM	8
Rapid Acceleration	1-2 instances in a trip	4
	3+ instances in a trip	7
Idle Time	More than 10 minutes per trip	5
Frequent Stops	More than 5 stops per trip	6
Seatbelt Usage	Not detected	10

This practical application of rule engines and scorecards in auto insurance illustrates how data-driven approaches can lead to more accurate and fair premium calculations, encouraging safer driving habits among policyholders.

Health Insurance: Personalized Premiums with Lifestyle Data

Health insurers are utilizing rule engines to fine-tune policy premiums based on individual lifestyle data. This process includes an initial rule set for knockout criteria followed by a comprehensive scorecard evaluation:

• Initial Rule Set with KO Criteria: The decision flow starts with a rule set that assesses knockout criteria. For instance, "If smoker and BMI > 30, then apply KO for standard rates," which may lead to alternative policy options or different pricing structures.
• Scorecard Analysis for Premium Adjustment: After applying KO criteria, the rule engine proceeds to the scorecard. This scorecard assesses factors like exercise frequency, smoking status, and BMI, where healthier habits lead to lower scores and potential premium discounts.
• Integrated Decision Flow: The integration of KO rules and the scorecard in the decision-making process ensures a holistic assessment of risk factors, aligning policy pricing with individual health profiles.
• Feedback for Healthier Lifestyles: Policyholders receive personalized feedback based on their scorecard results, encouraging healthier lifestyle choices.

This structured approach not only allows for more accurate premium setting but also aligns underwriting practices with reinsurance capabilities, ensuring sustainable risk management for the insurer.

Lifestyle Factor	Criteria	Points
Exercise Frequency	Daily Exercise	-5
	Exercise 3-4 times a week	-3
	Rarely or Never Exercises	5
Smoking	Non-Smoker	-5
	Smoker	10
Alcohol Consumption	Moderate (up to 2 drinks per day)	0
	Heavy (more than 2 drinks per day)	5
Body Mass Index (BMI)	18.5-24.9 (Normal)	0
	25 and above (Overweight/Obese)	5
Risky Behaviors	Participation in high-risk sports or activities	10
Stress Level	High Stress (as reported or measured)	5

Future of Insurance Underwriting

As technology advances, the role of AI and machine learning in rule engines will become more prominent, further refining the accuracy of behavioral pattern analysis in insurance underwriting and leading to even more customized insurance products.

Conclusion

The sophisticated integration of behavioral pattern recognition with traditional actuarial methods, facilitated by advanced rule engines, marks a new era in insurance underwriting. This approach not only enhances risk assessment accuracy but also paves the way for more personalized, data-driven insurance policies.