Claims severity

What Is Claims Severity?

Claims severity refers to the financial magnitude of an average claim filed under an insurance policy. In the realm of Insurance and Actuarial Science, it quantifies the average cost associated with an individual loss event, rather than how frequently such events occur. Understanding claims severity is crucial for insurers to accurately price their products, manage risk, and maintain financial stability. It is a key component in forecasting total losses incurred and is distinct from the number of claims.

History and Origin

The concept of quantifying and predicting financial losses from uncertain events has deep historical roots, dating back to ancient Babylon with the Code of Hammurabi, which included early attempts to quantify compensation for losses. Early forms of mutual aid societies in ancient Greece and Rome also laid groundwork for collective risk management. However, the formalization of analyzing and modeling loss severity evolved significantly with the development of actuarial science. The Casualty Actuarial Society (CAS), founded in 1914, was specifically established to address property and casualty risks, including the detailed analysis of claim costs, initially focusing on workers' compensation insurance.⁶ This professional body and its members have continually advanced the methods for understanding and predicting claims severity, moving beyond simple averages to sophisticated statistical models.

Key Takeaways

• Claims severity measures the average cost per insurance claim.
• It is a critical metric for underwriting, pricing, and reserving in the insurance industry.
• Factors influencing claims severity include inflation, repair costs, legal expenses, and the nature of the covered peril.
• Actuaries use specialized probability distribution models to forecast claims severity.
• Understanding claims severity helps insurers manage their overall risk assessment and ensure solvency.

Formula and Calculation

Claims severity is typically calculated as the total cost of losses incurred divided by the total number of claims over a specific period. While seemingly straightforward, the calculation relies on accurate and comprehensive data regarding past loss events.

The basic formula is:

Where:

• Total Losses Incurred represents the sum of all payments made for claims, including reserves for future payments, over a defined period. This can also include loss adjustment expense if included in the overall cost definition.
• Total Number of Claims is the count of individual loss events reported and processed during the same period.

This formula provides an average, but in practice, actuaries use more sophisticated data analytics and statistical methods to model the distribution of individual claim costs, recognizing that claim sizes can vary widely.

Interpreting Claims Severity

Interpreting claims severity involves more than just looking at the average cost. Insurers analyze the distribution of claims severity to understand the prevalence of small, medium, and large losses. A high average claims severity could indicate an increase in the cost of repairs, medical expenses, or litigation. For example, in property insurance, rising construction material costs directly impact the severity of property damage claims. In casualty lines, increasing legal settlements can drive up claims severity. Actuaries might use various statistical measures beyond the mean, such as the median, mode, and standard deviation, to gain a fuller picture of the claim cost landscape. This detailed analysis helps in setting appropriate premium levels and maintaining adequate reserves.

Hypothetical Example

Consider a hypothetical auto insurance company, "DriveSure," analyzing its claims data for the past year.

1. Total Losses Incurred: DriveSure paid out $15,000,000 in covered losses, including repair costs, medical bills, and legal expenses.
2. Total Number of Claims: During the same period, DriveSure processed 5,000 individual auto accident claims.

Using the formula for claims severity:

This means that, on average, each auto insurance claim cost DriveSure $3,000. This figure is crucial for DriveSure's underwriting department to assess profitability and for its actuaries to refine future premium rates for different types of policyholders.

Practical Applications

Claims severity is a foundational metric with numerous practical applications across the insurance industry:

• Pricing and Rate-Making: Insurers use historical claims severity data to project future average claim costs and set appropriate premiums for their products. This ensures that the premiums collected are sufficient to cover expected losses.
• Reserving: Accurate estimates of claims severity are vital for establishing adequate loss reserves. These reserves are funds set aside to pay for future claims and existing claims that have been reported but not yet fully settled.
• Risk Management and Capital Allocation: By understanding how large individual losses can be, insurers can better manage their overall risk exposure and allocate capital effectively. High-severity events often require sophisticated reinsurance strategies to transfer risk.
• Product Development: Analysis of claims severity helps in designing new insurance products or modifying existing ones to better suit market needs and evolving risk landscapes. For instance, in property insurance, the rise in costs associated with severe convective storms (SCS), where hail can account for a significant portion of losses, directly influences how policies are structured and priced.⁵
• Catastrophe Modeling: For perils like hurricanes, earthquakes, and floods, catastrophe modeling incorporates claims severity to estimate potential losses from extreme events. In 2023, global insured losses from natural catastrophes exceeded $100 billion for the fourth consecutive year, with events like the earthquake in Turkey and Syria and severe convective storms being major drivers.⁴ This highlights the immense financial impact of high-severity events.
• Regulatory Compliance: Regulatory bodies, such as the National Association of Insurance Commissioners (NAIC) in the U.S., collect and analyze claims data to ensure that insurers are financially sound and that their pricing is fair.³ The NAIC publishes reports that include components like losses incurred to provide transparency on profitability by line and by state.²

Limitations and Criticisms

While claims severity is a vital metric, it comes with certain limitations and faces criticisms:

• Reliance on Historical Data: Claims severity models often rely on past data, which may not always accurately predict future trends, especially with rapidly changing risks such as climate change impacts or new technologies.
• Sensitivity to Outliers: A few exceptionally large claims can significantly skew the average claims severity, making the average less representative of typical claim costs. This heavy-tailed nature of claim severity distributions can make accurate modeling challenging.
• Data Quality and Completeness: The accuracy of claims severity calculations is dependent on the quality and completeness of reported claim data. Incomplete or inconsistent data can lead to skewed results.
• Exclusion of Zero Claims: Claims severity only considers claims that have occurred and resulted in a payment. It does not account for the instances where no claim is filed, which is addressed by claims frequency.
• Model Complexity: Modeling claim severity accurately, particularly the tail (large losses) of its distribution, can be highly complex. Traditional statistical models like Generalized Linear Models (GLMs) can sometimes fail to adequately capture the heterogeneity and specific characteristics of severity data, such as multimodality or heavy tails, potentially leading to inaccuracies in ratemaking and reserving.¹ This highlights the ongoing need for advanced data analytics and modeling techniques in actuarial science.

Claims Severity vs. Claims Frequency

Claims severity and claims frequency are two distinct yet interconnected concepts fundamental to Insurance and Actuarial Science.

While claims severity focuses on the monetary value per claim, claims frequency quantifies how often claims occur for a given portfolio of policies. For example, a company might have a high claims frequency (many small claims) but low claims severity, or a low claims frequency (few claims) but high claims severity (a few very expensive claims). Both metrics are essential for calculating the "pure premium," which is the expected claims cost per unit of exposure. This pure premium is typically modeled as the product of expected claims frequency and expected claims severity.

FAQs

Why is claims severity important for insurance companies?

Claims severity is crucial because it directly impacts an insurance company's profitability and solvency. By understanding the average cost of a claim, insurers can set accurate premiums, establish appropriate reserves to cover future payments, and manage their overall risk exposure. Without accurate severity assessments, an insurer might underprice its policies, leading to financial instability.

How do factors like inflation affect claims severity?

Inflation can significantly impact claims severity. As the cost of goods and services rises (e.g., medical treatments, auto parts, construction materials), the cost to repair damages or compensate for losses increases. This directly drives up the average cost of each claim, contributing to higher claims severity. Insurers must account for inflationary trends in their statistical models to project future claims costs accurately.

What is the role of actuaries in analyzing claims severity?

Actuaries are central to analyzing claims severity. They use advanced statistical and mathematical techniques to model the distribution of individual claim costs, forecast future trends, and assess the impact of various factors on severity. Their work directly informs underwriting decisions, premium setting, and the establishment of adequate reserves, ensuring an insurance company's financial health. They often employ specific probability distribution models to fit claim severity data, which often exhibit heavy-tailed characteristics.

Can claims severity be influenced by human behavior?

Yes, human behavior can influence claims severity. For instance, in auto insurance, factors like distracted driving or aggressive driving might lead to more severe accidents, thus increasing the average cost per claim. Similarly, attitudes towards litigation or the propensity to seek extensive medical treatment can impact the overall cost of claims in various lines of business. Behavioral economics and data analytics are increasingly used to understand these influences in risk management.

Is higher claims severity always a negative indicator for an insurer?

Not necessarily. While a sharp increase in claims severity can signal rising costs and potential profitability challenges, it's essential to consider the context. For example, an insurer specializing in large commercial risks might naturally have higher claims severity than one focused on personal auto policies. The key is whether the insurer has adequately priced its premiums and established sufficient reserves to cover these higher average costs. A stable, predictable claims severity within expectations is generally a positive sign.