Transforms loss run files into detailed, AI-driven reports that provide insurers and MGAs with actionable insights on claim severity, carrier loyalty, delayed claims, and large losses. This enables more accurate risk assessment by categorizing coverage-specific losses and identifying trends, improving underwriting efficiency and decision-making.
Funding
$1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Underwriters at insurance carriers and MGAs face challenges in efficiently extracting and interpreting data from unstructured loss run documents. This manual process is time-consuming, prone to errors, and hinders accurate risk assessment, ultimately impacting underwriting efficiency and decision-making.
Solution
Pibit.ai provides an AI-powered platform that transforms loss run files into structured, actionable insights for commercial underwriting. The platform automates the extraction and categorization of data from loss run documents, providing detailed summaries of policy history and loss trends. By leveraging natural language models, Pibit.ai identifies coverage-specific losses, accident trends, claim severity, carrier loyalty, and delayed claims. This enables underwriters to make informed decisions, reduce risk, and improve underwriting efficiency.
Target Audience
Pibit.ai primarily targets property and casualty (P&C) insurance carriers and managing general agents (MGAs) seeking to improve underwriting efficiency and accuracy.
Features
- Automated loss run data extraction and transformation into structured reports
- AI-powered categorization of coverage and accident descriptions for precise risk assessment
- Identification of key loss trends, including claim severity, carrier loyalty, and delayed claims
- Detailed loss summaries providing a comprehensive view of policy history
- Support for various loss run formats, ensuring seamless processing of complex cases