Claims History & Loss Data
Workplace Safety
Business & Industry Coding Systems
Geospatial & Environmental Risk
The 2025 Federato State of Underwriting Report is Here. Get it Now >
Explore the Federato platform at your own pace with our free interactive product tour.
Take a tourEvery insurance policy relies on accurate classification to evaluate and price risk. For many types of coverage, this begins with assigning a class code that reflects the insured party's operations or exposure type. These codes play a central role in underwriting, pricing, reserving, and regulatory reporting.
Some class codes carry more uncertainty than others. These are often referred to as high-risk class codes. They present unique challenges for underwriters because of their complexity, variability, or the nature of the exposures they represent.
Understanding what makes a class code high-risk is a necessary first step in assessing these exposures accurately. Let's explore the key considerations when underwriting these challenging classifications.
High-risk class codes are insurance classifications used to group businesses or exposures with a higher likelihood of loss. These codes represent industries or operations that are more complex, volatile, or historically prone to claims. You'll find them commonly used in workers' compensation, general liability, and commercial auto insurance.
Unlike standard class codes, high-risk codes involve greater uncertainty. This might be due to limited historical data, rapidly changing risk factors, or heightened regulatory scrutiny.
Examples of high-risk class codes include:
Class codes help ensure consistency in premium calculation and support loss reserving, underwriting guidelines, and regulatory filings. They're typically defined by industry classification systems like the National Council on Compensation Insurance (NCCI) for workers' compensation or Insurance Services Office (ISO) for general liability.
What makes a class code "high-risk"?
When a business is incorrectly classified, the consequences affect both insurers and policyholders. Misclassification can lead to premiums that don't match the actual risk level—resulting in either underfunded claims or excessive costs.
Premium adequacy depends on matching each exposure to the right class code. Inaccurate classification distorts an insurer's portfolio performance and affects how funds are reserved for future claims. These errors also impact reinsurance arrangements, which often rely on classification data for treaty terms and pricing.
Over time, classification errors compound. What seems like a minor mistake on a single policy can distort trends across an entire book of business when repeated.
The consequences of inaccurate classification include:
Underwriters follow a structured process to assess risks and determine appropriate premiums. Class codes serve as the starting point, providing a reference for understanding the type of exposure being insured.
Each code links to a base rate developed from historical loss data. Underwriters then adjust these rates using modifiers, credits, or debits based on the specific risk characteristics.
The evaluation combines quantitative data (loss history, payroll, revenue) with qualitative judgment (operational details, risk controls, management quality). This approach allows underwriters to differentiate between businesses that share the same class code but have different risk profiles.
Even experienced underwriters face challenges with high-risk class codes. These classifications involve complex exposures and limited data, which can lead to errors. Here are three common pitfalls:
Business operations change over time, but class codes often remain static. When classifications aren't updated to reflect new activities, they no longer represent the actual risk.
For example, a manufacturer might shift from producing mechanical parts to assembling lithium-ion batteries. If the class code isn't updated, the risk profile becomes misrepresented.
Regular classification reviews help identify these changes. These reviews include conversations with the insured, evaluation of current operations, and analysis of recent claims to determine if reclassification is needed.
Some high-risk class codes lack sufficient historical data, making it difficult to predict future losses. This is common with newer industries or specialized operations.
To address data gaps, underwriters can use:
Industry benchmarking helps identify outliers by comparing a specific risk to similar businesses. This comparison might include loss ratios, claim severity, and premium adequacy across comparable operations.
Classification decisions involve multiple teams—underwriting, claims, and actuarial. When these groups don't align on definitions or procedures, inconsistencies emerge.
For instance, claims teams might interpret exposures differently than underwriters, creating discrepancies in how losses are coded. Actuarial teams might base models on assumptions that don't match current underwriting practices.
Cross-functional collaboration reduces these problems. Standardized documentation, shared definitions, and regular team reviews ensure everyone works with consistent information.
Insurance underwriting faces a fundamental tension: creating detailed classifications versus having enough data for reliable analysis. More specific class codes describe risks more precisely, but smaller groups may lack sufficient data for credible decisions.
Homogeneity within class codes means all risks in a class should behave similarly. When risks in the same class have very different loss patterns, pricing becomes less accurate.
Sub-categories help separate different exposure types that would otherwise be grouped together. They're useful when operations, exposures, or loss histories vary significantly within a class code.
For example, in construction, framing contractors and roofing contractors might be separated due to different fall risks. In commercial auto, local delivery drivers might be grouped separately from long-haul truckers.
The right level of detail depends on:
Credible analysis requires sufficient data. With too few data points, predicting future losses becomes difficult. This challenge is common in emerging industries or newly defined class codes.
To address sparse data, underwriters can:
For example, in cyber liability, some insurers use security firm data alongside claims history to better understand exposure patterns.
Classification volatility occurs when small data changes cause large shifts in risk classification. This creates inconsistent underwriting decisions and unstable pricing.
Smoothing techniques help reduce volatility. These include credibility weighting, which blends observed data with expected values from larger groups. For a small trucking company with limited experience, underwriters might combine its data with industry averages to minimize the impact of individual claims.
Technology is transforming how insurers handle high-risk class codes. Traditional methods rely heavily on manual processes and expert judgment. Modern approaches use artificial intelligence and analytics to support human decision-making with faster data processing and pattern recognition.
Predictive models estimate future risk behavior based on historical patterns. These tools enhance traditional class code systems by refining how insurers evaluate loss likelihood and impact.
Effective models incorporate variables like:
For example, a commercial auto model might combine telematics data, driving behavior, and route information to differentiate between two trucking companies with the same class code.
External data sources provide additional context not captured in an insurer's internal systems. These might include property records, vehicle information, business licenses, and environmental risk scores.
When evaluating third-party data, underwriters consider:
Risk assessment is evolving from one-time evaluation to continuous monitoring. Instead of relying on a single classification decision, insurers can track how risks change throughout the policy term.
Real-time monitoring detects changes in exposure, behavior, or external conditions. A shift in business activities, new regulatory issues, or an increase in claims might signal the need for reassessment.
Modern underwriting platforms help analyze portfolios at scale. They can group accounts by class code, region, or loss trends and highlight areas that deviate from expected results.
High-risk class codes require ongoing attention because exposures and operations change over time. New technologies, business practices, and external conditions alter how risks behave.
Reclassification involves reviewing current operations to determine whether the assigned code still reflects the actual exposure. This process compares original classifications with updated information from inspections, applications, or operational changes.
High-risk classes typically need review annually or semi-annually due to their volatility. Industries that evolve quickly or show changing loss patterns may require more frequent assessment.
A good reclassification program includes:
Industries evolve in response to technology, regulation, and social changes. Traditional class codes may not account for these shifts, creating coverage gaps or pricing inaccuracies.
The gig economy, for example, includes workers who operate as independent contractors across multiple platforms. These roles often don't align with standard employment-based codes. Similarly, remote work has changed the risk profile for many office-based jobs.
Underwriters can address these changes by:
Successful underwriting of high-risk class codes requires balancing traditional expertise with modern data analytics. Human judgment provides insight into business operations and risk context. Data analytics improves accuracy and consistency by identifying patterns across large portfolios.
Integrated platforms streamline this process by connecting data sources, highlighting relevant insights, and aligning classifications with portfolio goals. These systems give underwriters a single environment to assess submissions, verify codes, and monitor performance over time.
Accurate class code management affects every aspect of insurance operations—from pricing and reserving to reinsurance and portfolio management. When classifications reflect real-world risk accurately, insurers make more consistent decisions and reduce misclassification exposure.