Loss runs provide insurers with a detailed history of past claims, offering critical insights that underwriters rely on for risk assessment and pricing decisions. While insurers typically provide complete loss run reports, underwriters often struggle to efficiently extract, summarize, and review the vast amounts of data within them. Given the highly manual nature of loss run processing, underwriters may only review a fraction of the available data—limiting their ability to make fully informed decisions and increasing the risk of oversight.
This inefficiency impacts underwriting teams by slowing down workflows, increasing decision-making time, and contributing to operational bottlenecks. As a result, poor loss run data management can lead to mispriced policies, wasted resources, and strained broker-client relationships—all of which harm revenue. Addressing these hidden costs is the first step toward modernizing the underwriting process.
The Cost of Incomplete or Inaccurate Loss Runs
Incomplete or inaccurate loss run data can create significant challenges for insurers, impacting efficiency, costs, and underwriting decisions.
1. Inaccurate Risk Profiling
Manually and efficiently extracting and analyzing all data from loss runs is a major challenge. Loss run reports often come in varied formats, making it difficult to consistently structure and review all relevant information. Without a standardized method for extracting data, underwriters may only assess a portion of the available details, leading to potential mispricing of policies.
While missing loss run data can usually be requested from brokers or agents, the real issue is ensuring that all provided data is thoroughly examined. Underwriters who cannot efficiently process large volumes of information may inadvertently overlook critical insights, increasing uncertainty in risk assessment and making it harder for carriers to maintain a balanced and profitable portfolio.
2. Time-consuming Data Cleanup
During preliminary qualification, teams often spend excessive time manually cleaning and structuring loss run data. Since almost every carrier has a different format for their loss runs, underwriting assistants and underwriters will oftentimes spend countless hours manually reviewing and re-keying loss data into their own spreadsheets or rating systems.
When loss run data comes in PDF format, this could take hours. When it comes in Excel format, the order of columns and data presentation will usually be different, and again, it could take hours. And finally, when loss data is manually reviewed and entered, it often contains missing information that will need to be requested from the broker/agent and populated.
The time and effort spent on these tasks divert resources from more strategic activities like risk analysis and decision-making. The inefficiency caused by data cleanup not only slows down the underwriting process but also adds operational costs.
3. Delays in Decision-making
Slow data processing and manual intervention lead to significant delays in underwriting decisions. Brokers and policyholders expect quick turnaround times for quotes and policy approvals, but incomplete or unstructured loss run data can cause bottlenecks.
These delays affect an insurer’s ability to provide timely responses, potentially resulting in lost business. Clients may become frustrated and turn to competitors that offer faster and more seamless underwriting experiences. Reducing decision-making delays is crucial for maintaining a strong reputation and ensuring customer satisfaction in a highly competitive insurance market.
4. Increased Underwriting Costs
Every hour spent manually processing loss run data adds to an insurer’s operational cost. The costs associated with labor-intensive data extraction, verification, and correction add up quickly. Additionally, when underwriting teams have to revisit submissions due to incorrect or missing data, it further extends processing times and increases the cost of handling each policy.
These inefficiencies increase underwriting expense ratios and decrease underwriter job satisfaction, making it more difficult for carriers to maintain cost-effective operations while delivering high-quality service.
5. Higher Risk Exposure
When loss run data is incomplete, underwriters may unknowingly take on high-risk policies without fully understanding the associated liabilities. Missing claims history can obscure past trends, preventing insurers from accurately assessing potential exposure. This raises the likelihood of unexpected losses, as policies may be underwritten without accounting for historical claim patterns.
A lack of reliable data leads to poor risk selection, which can have long-term financial consequences for an insurer’s portfolio. Addressing these gaps in data is essential for minimizing exposure and ensuring sustainable underwriting practices.
6. Broker and Client Frustration
Incomplete or inaccurate loss run data creates friction in broker and client relationships. When underwriters regularly take days or weeks to review a loss run, and then finally request missing information that could have been identified and requested up-front, brokers may become frustrated. Sometimes they will need to go back to their insureds to gather the missing information, and the long timelines will not reflect well on them.
If data issues persist, brokers may seek out competitors that provide a smoother, more seamless underwriting experience. Improving data accuracy and accessibility strengthens broker and client relationships, helping insurers maintain valuable partnerships and drive long-term business growth.
7. Underwriting Team Frustration
Manual loss run processing is tedious and exhausting, leading to underwriting team frustration. In practice, this is the kind of work that can really only be done in short bursts before fatigue sets in. This inefficiency not only drains productivity but also makes it harder to attract and retain new talent in the industry.
Younger underwriters, in particular, expect to have modern technology at their fingertips. Without AI-powered solutions to handle repetitive tasks and streamline workflows, underwriting roles may become less appealing to new professionals, exacerbating hiring challenges for insurers. To remain competitive, insurers must invest in technology that reduces monotony and allows underwriters to focus on higher-value decision-making.
The Role of AI in Loss Run Processing
Advancements in artificial intelligence have made it possible to streamline loss run processing, reducing the burden of manual data handling. AI-driven solutions can automatically extract, clean, and structure loss run data in minutes, providing underwriters with accurate, structured information almost instantly.
By improving these processes, insurers can significantly cut down processing times, improve data accuracy, and strengthen risk assessment capabilities. AI-powered loss run ingestion allows underwriting teams to focus on value-driven tasks rather than data entry and cleanup.
Transforming Challenges into Competitive Advantages
The hidden costs of incomplete loss runs can significantly impact underwriting efficiency, profitability, and client relationships. From inaccurate risk assessments to increased operational costs and delayed decision-making, poor data management creates unnecessary challenges for insurers.
However, by embracing AI-powered loss run ingestion solutions, like Groundspeed by Insurance Quantified, insurers can modernize their loss run processing, improve data accuracy, and streamline underwriting workflows. Investing in automation is not just about reducing inefficiencies—it’s about gaining a competitive advantage in a rapidly evolving insurance landscape.
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