Loss Run Reports
Buy and sell loss run reports data. Claims history reports that show patterns in losses over time. Pricing AI and risk platforms need longitudinal loss data.
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Find Me This Data →Overview
What Are Loss Run Reports?
Loss run reports are formal documents that summarize an organization's claims history over a defined period, typically three to five years. Issued by current or past insurance carriers, these standardized and verifiable reports are required by insurers during underwriting and are essential for obtaining new commercial coverage quotes. Loss runs apply across nearly all commercial insurance lines, including General Liability, Business Property, Workers' Compensation, Professional Liability, Cyber, and Commercial Auto. Loss runs help insurers and brokers understand patterns in claims—frequency, severity, and types of losses—which directly inform pricing decisions, coverage determinations, and risk management strategies. For data buyers, longitudinal loss run data is critical: AI and risk platforms use these historical patterns to assess risk exposure, predict future claim likelihood, and optimize pricing models. However, loss run reports often arrive as lengthy PDFs or spreadsheets from multiple carriers with different formats and terminologies, making manual analysis time-consuming and error-prone.
Market Data
7+ lines (General Liability, Property, Workers' Comp, Professional Liability, Cyber, Business Owner's Policies, Commercial Auto)
Coverage Types Using Loss Runs
Source: Vouch
3–5 years
Typical Report Lookback Period
Source: Vouch
Hours to days per account
Manual Analysis Time Burden
Source: Kolena
Dozens of pages from multiple carriers
Documents Per Account
Source: Kolena
Who Uses This Data
What AI models do with it.do with it.
Insurance Underwriters & Risk Assessors
Underwriters rely on loss run data to evaluate risk exposure, determine premiums, and make coverage decisions. Clean loss run records signal lower exposure and enable more competitive pricing, while frequent or high-severity claims require deeper investigation into root causes and remediation efforts.
AI & Predictive Risk Platforms
Insurtech and risk modeling platforms ingest longitudinal loss run data to build predictive models, identify claim patterns, and automate risk scoring. This enables faster underwriting, improved pricing accuracy, and proactive risk management recommendations.
Insurance Brokers & Agents
Brokers use loss runs to shop clients across multiple carriers and negotiate better terms. They analyze patterns to identify coverage gaps, recommend loss control improvements, and communicate risk profile to prospective insurers.
Claims Management & Loss Prevention
Organizations use their own loss run history to track claim trends, identify recurring loss drivers, and implement targeted loss prevention strategies to reduce future exposure and improve insurability.
What Can You Earn?
What it's worth.worth.
Individual Loss Run Reports
Varies
Pricing depends on report depth, lookback period, and number of claims included. Enterprise platforms may license bulk datasets.
Aggregated Loss Run Datasets
Varies
Bulk historical loss data sold to insurers, underwriters, and risk platforms. Pricing scales with dataset size, granularity, and exclusivity agreements.
API Access & Data Feeds
Varies
Recurring subscription for real-time or periodic loss run feeds integrated into underwriting systems. Typically tiered by volume and update frequency.
What Buyers Expect
What makes it valuable.valuable.
Standardized Format & Verified Data
Loss runs must be standardized, dated, and issued directly by insurance carriers to be verifiable during underwriting. Brokers and insurers depend on the formal carrier stamp for credibility.
Complete Claims History
Comprehensive coverage of all claims over the lookback period (typically 3–5 years), including claim date, amount, type, status, and resolution. Gaps or omissions undermine risk assessment.
Multiple Carrier Integration
Data should consolidate loss runs from multiple insurers into a single, unified view. Buyers need normalized terminology and consistent field mappings across carriers to avoid manual reconciliation.
Accurate Pattern & Trend Analysis
Loss run data must be clean and consistent enough for AI models to detect frequency, severity, and loss type patterns. Data quality errors propagate through risk algorithms and skew pricing.
Companies Active Here
Who's buying.buying.
Purchase loss run data to streamline underwriting, assess risk exposure, and set competitive premiums. Use historical patterns to inform coverage decisions and policy terms.
Ingest longitudinal loss data to build predictive risk models, automate underwriting workflows, and provide real-time risk scoring and recommendations to carriers and brokers.
Use loss run data to compare client risk profiles across multiple carriers, negotiate better rates, and identify coverage gaps and loss prevention opportunities.
FAQ
Common questions.questions.
How long do loss run reports cover?
Loss run reports are typically issued for a three to five year lookback period, though the specific range can vary by carrier and policy type. This timeframe allows underwriters to assess claim trends and risk patterns over a meaningful historical window.
Why do insurers require loss run reports?
Loss runs are essential to underwriting because they provide a standardized, verifiable record of an organization's claims history. Insurers use this data to assess risk exposure, determine pricing, evaluate coverage decisions, and understand patterns in claim frequency, severity, and types.
What makes loss run data valuable for AI and risk platforms?
Loss run data is longitudinal and contains structured information about historical claims, enabling AI models to identify patterns, predict future claim likelihood, and optimize pricing. Clean, standardized data across multiple carriers allows platforms to build accurate predictive models and automate risk assessment.
What are the challenges with using loss run data today?
Loss runs often arrive as lengthy PDFs or spreadsheets from multiple carriers with different formats and terminology. Manual analysis is labor-intensive, taking hours or even days per account, and is prone to errors. Data consolidation and normalization across carriers remains a significant operational burden for underwriters and brokers.
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