“Digitizing” means leveraging technology to enhance traditional manual processes. But without the right approach, the digitization process can introduce new challenges instead of solving the old ones.
Nowhere is this more evident than in loss runs—the historical claims data that underwriters rely on for risk assessment and pricing. While technology can streamline parts of the process, effective digitization requires structuring, extracting, and normalizing vast amounts of unstructured data to make it actionable for underwriters.
Many underwriting teams have attempted digitization but found that traditional approaches take too long, are overly complex, or lack focus—often leading to more inefficiencies instead of solving them. Implementing AI for AI’s sake doesn’t necessarily improve workflows; instead, it can introduce new obstacles that slow down underwriting operations.
A focused AI-powered loss run ingestion solution that starts simple and scales over time can help address these challenges. By seamlessly translating complex, inconsistent documents into structured, actionable data, insurers can streamline workflows, improve accuracy, and drive efficiency.
Why Is Digitizing Loss Runs Challenging?
1. A Vast and Varied Landscape of Loss Run Formats
Each carrier structures loss runs differently, reflecting differences in product lines, business priorities, and internal reporting standards. Some formats contain high-level summaries, while others include so much data that it cannot all be extracted and evaluated.
Additionally, terms used to label data fields vary widely, and the same term can mean different things depending on the insurer producing the document. An example might be the financials on a loss run; one carrier may calculate a certain value differently than another.
Without a standardized structure, underwriters must manually interpret and reorganize each loss run format, a process that introduces inefficiencies and the potential for errors. A successful digitization solution must account for this variability while maintaining accuracy and consistency.
Learn more about how AI reduces inefficiencies in underwriting and improves loss ratios.
2. Lack of Industry-Wide Standardization
In many industries, digital transformation efforts are supported by shared data standards. In insurance, however, formats are often not standardized. While the Association for Cooperative Operations Research and Development (ACORD) maintains standardized forms, that’s just one form. Multiple, non-standardized forms make automation difficult. A one-size-fits-all approach simply does not work when every document follows a different set of rules. Instead, an advanced solution must be adaptable enough to recognize and process countless document variations effectively.
3. The Need for Advanced Parsing and Normalization
To bring loss runs into a usable format, insurers need a solution that can do more than extract unstructured text. Digitization must involve deep document understanding, translating ambiguous, inconsistent structures into a universal schema that underwriters can rely on.
AI-powered loss ingestion achieves this by leveraging:
- Computer vision and OCR preprocessing that extract text and structural elements from PDFs and other formats, converting them into a machine-readable representation.
- A vast library of loss run formats observed “in the wild,” allowing rapid identification of known formats and efficient cataloging of new ones.
- Document parsing models that automatically classify and structure data points based on format-specific naming conventions and structures.
- A comprehensive universal data schema that aligns extracted data into a standardized framework, ensuring usability across underwriting teams.
4. Filling in the Gaps with Intelligent Data Enrichment
Loss runs don’t always tell the whole story. Missing data points, inconsistencies, and ambiguous values are common in these documents. To create a complete and reliable dataset, insurers need a solution that can intelligently predict missing details and normalize data fields into standardized types and formats.
AI-powered loss run ingestion incorporates extensive business logic to:
- Infer missing data points using AI-driven analysis.
- Normalize key fields to ensure consistency across all extracted data.
- Standardize dates, currency values, and categorical data for seamless downstream processing.
5. Ensuring Accuracy Through Human Oversight
While AI quantifiably improves efficiency, human oversight remains essential for maintaining data integrity. Exception handling, quality control, and expert validation play a crucial role in ensuring underwriting teams can trust the data they receive.
An effective AI-powered loss run ingestion system increases accuracy by integrating a human-in-the-loop approach that:
- Flags anomalies and inconsistencies for expert review.
- Ensures the highest standard of reliable and complete data outputs.
- Improves AI algorithms through continuous feedback loops.
How AI-Powered Loss Ingestion Solves These Challenges
Advanced AI-powered solutions are built to handle the complexity of loss run digitization at scale. These systems don’t just extract data—they transform it into structured, usable insights that underwriting teams can trust. By leveraging a combination of machine learning, expert-driven business logic, and human validation, AI-powered ingestion eliminates inefficiencies and empowers insurers to make smarter, faster underwriting decisions.
Not all digital solutions are created equal, especially when it comes to underwriting. Without the right partner—one that understands the intricacies of loss run ingestion—insurers risk compounding inefficiencies rather than eliminating them. The challenge isn’t just extracting data; it’s structuring, validating, and enriching data to ensure underwriters can trust the information they rely on for decisions.
Achieve Efficiency and Accuracy in Loss Run Digitization
AI-powered loss ingestion ensures that underwriters receive clean, normalized, and enriched loss run data without the headache of manual processing. By bridging the gap between raw, unstructured documents and actionable insights, these solutions are setting a new standard for underwriting efficiency in the digital era.
Digitizing loss runs isn’t just about automation—it’s about creating structured, high-quality data that fuels better underwriting outcomes. With insurers working in a landscape of widely varying document formats and no shared industry standard, having the right technology and the right team and processes in place makes all the difference.
Groundspeed by Insurance Quantified provides AI-powered submission ingestion to tackle these challenges head-on, providing underwriters with structured, accurate, and actionable loss run data for faster, more confident decision-making. Our company is 100% focused on commercial insurance, and our clients benefit from dedicated account managers with deep underwriting knowledge of loss runs, ensuring they receive tailored solutions that align with their specific needs. Learn more about how we do it.
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