SBIR Phase I: Intelligent Interactive Guidance System for Litigated Insurance Claims

This Small Business Innovation Research (SBIR) Phase I project enhances the efficiency, fairness, and cost-effectiveness of the United States' property and casualty insurance claims processes. This project aims to develop an advanced, artificial intelligence (AI) powered guidance system that will transform how litigated insurance claims are managed and resolved. By enhancing the decision-making process of claims professionals with automated, evidence-backed guidance, the system will significantly reduce the time and expense currently required to resolve claims, resulting in quicker payouts to claimants and decreasing the burden of legal costs. The system's innovative approach will assist in identifying critical case information, supporting claim professionals in making more informed decisions. The project has the potential to improve the overall transparency and reliability of the claims litigation processes, engendering greater trust in the insurance system. Additionally, by streamlining operations, it could lead to more efficient use of resources within the insurance industry, lowering insurance premiums for consumers and businesses. This SBIR Phase I project represents an opportunity to significantly improve the processing and handling of litigated insurance claims. The project’s research objectives include the development of a novel approach for information extraction from massive unstructured data collections typical in insurance claims and summarization frameworks for presenting the extracted information, enabling a concise yet comprehensive view of complex claims data. The project aims to design a visualization interface that aids understanding and facilitates more informed decision-making by claims professionals. The research applies cutting-edge AI and machine learning techniques to these objectives, expanding past the boundaries of current capabilities in data analytics within the insurance industry. The anticipated technical results include demonstrating the feasibility of this innovative system to quickly and accurately present relevant decisional information from a broad array of data, providing users with essential insights for making decisions. By improving how information is processed, summarized, and presented, the project is expected to lead to better, faster decisions in litigated insurance claims management, setting a new standard for technological applications in the field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.