Jul 20, 2024
Navigating and interpreting loss runs presents numerous challenges for insurers, brokers, and policyholders. The inherent complexity of these documents, coupled with a lack of standardization and data quality issues, makes accurate and efficient processing difficult. Understanding these challenges is essential for developing effective solutions and improving the overall functionality of loss run management.
No Standard Format: Due to the diversity in lines of business, coverage, and claim processing, loss runs vary significantly between insurers.
Variable Terminology: Terms used by insurance companies can differ, leading to confusion. For example, “Total Losses” might be listed as “Total Incurred Losses” or “Total Claims” on another carrier’s loss run.
Diverse templates: Loss runs can appear in various templates such as key-value pairs, simple tables, or multi-header tables, complicating interpretation.
Data Representation: There are inconsistencies in how data is presented, such as accident locations being recorded with varying levels of detail.
Inconsistent Use of Symbols: Money that the insurance carriers are able to recover through subrogation or salvage, known as recoveries might be indicated with different symbols (negative sign, parentheses), adding to the complexity.
Interpreting loss runs can be challenging due to the lack of standardization. The same information can be interpreted differently, making it difficult to define automation rules. Loss runs often need to be read contextually, requiring a special skill set, and an understanding of the entire document and its nuances.
Loss runs frequently suffer from data quality issues:
Inconsistent Representation of No Losses: Different carriers represent “No Known or Reported Losses” (NKORL) in various ways, such as text statements, separate letters, or in Loss Runs templates with all information as empty.
Crude Methods of Generation: Many loss runs are created using basic methods like copy-pasting from claim systems, leading to formatting and data quality issues.
Manual Data Entry: Manually processing and analyzing large volumes of loss run data is time-consuming and prone to human error.
Once data is extracted from loss runs, integrating it into existing insurance systems poses another challenge. This integration is crucial for seamless operations but comes with its own set of hurdles:
System Compatibility: Different systems may have compatibility issues, making it difficult to integrate loss run data without extensive entity normalization.
Data Mapping: Ensuring that data fields from loss runs are correctly mapped to corresponding fields in the target system is essential to maintain data integrity.
Workflow Disruption: Integrating new data sources can disrupt existing workflows and require retraining of staff.
Insurance carriers may currently see little direct benefit in standardizing loss run reports, especially for insured parties who might switch providers. This further complicates the creation of consistent, standard and accurate loss run documents across the commercial and specialty insurance industry.
Traditional methods of processing loss runs include manual data entry and semi-automated processes using Optical Character Recognition (OCR) and rule-based systems. However, these methods have significant limitations:
Time-Consuming: Manual entry of large loss run documents can take days or weeks.
Error-Prone: Manual processes are susceptible to errors, such as typos that can drastically alter claim amounts.
Inflexible: Standard Operating Procedures (SOPs) may not cover all scenarios, particularly non-standard ones.
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