Transforming Insurance Loss Run Risk Assessment
Industry
Commercial Insurance
Challenge
The company faced the challenge of extracting and analyzing critical data from lengthy, complex, and highly variable loss-run documents. Manual processes were slow and error-prone, leading to delays in policy issuance, incomplete data use, and inaccurate risk assessments. Existing software solutions could not keep up with the complexity or scale, leaving the company exposed to unnecessary risk.
Results
Using Docugami’s AI-powered Document Engineering, the company was able to process loss-run documents much faster and with greater accuracy. The platform identified and organized critical data across hundreds or even thousands of documents, while providing full transparency and traceability back to the original source. Analysts could quickly verify and update data points, and the system improved continually with each correction. The result was deeper risk insights, more accurate pricing, and reduced time and effort compared to manual processes.
KEY FEATUREs
XML Knowledge Graphs
Automated Data Extraction Across Large Document Sets
Transparent, Verifiable Reports with Source Citations
Continuous Learning Based on Analyst Feedback
The Challenge
Loss run documents contain vital information that can help insurance providers assess the potential risks associated with a potential customer, and set appropriate coverages and prices based on a deep understanding of risk. Unfortunately, loss-run documents are long, complex, and variable, making it extremely difficult for companies to identify and extract the needed data in an accurate and efficient manner.
Manual data entry and processing were time-consuming and error-prone, leading to delays in policy issuance and inaccuracies in risk assessment. The company had tried a number of software approaches, but they all fell short due to the complexity of loss-run documents. The inability to harness the full potential of loss-run data in a timely manner left the company reliant on partial data and exposed to unnecessary risk.
The Solution
The breakthrough came when the company tested Docugami’s AI-powered Document Engineering platform. Using multiple forms of AI, Docugami was able to understand the structure of the company’s diverse sets of loss-run documents, despite the wide diversity of document styles, structures, editing, and ways of organizing the underlying data. Docugami was able to detect the structural similarities across huge document sets, then identify, extract, and organize the critical information across hundreds or even thousands of individual loss-run documents.
The Results
Docugami was able to export the key data into highly organized and transparent reports, with citations for every extracted item directly back to the specific location in the individual loss run document from which it was generated. Unlike other Generative AI tools, Docugami’s full transparency gives frontline insurance analysts the ability to query, verify, and update individual data points quickly and easily. The company’s unique instance of Docugami actually “learns” every time a member of the team updates a generated data point, improving system accuracy with this feedback. This learning is specific to this insurance company only – learning based on their Loss Run documents is never shared with another Docugami customer.
Utilizing Docugami, the company was able to reduce the time required to generate insights from loss run documents significantly, while also reflecting their own individual data needs. Given the inherent complexity of loss run documents, some human intervention was still required to achieve maximum accuracy and fine-tune the software to their preferences, but the learning curve was swift, with no disruption to ongoing operations. Data extraction and analysis from Loss Run documents became faster and more accurate, enabling the company to gain deeper insights into risk profiles and tailor policies to meet the unique needs of its diverse clientele.