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

这个小企业创新研究（SBIR）第一阶段项目提高了美国财产和意外伤害保险索赔流程的效率，公平性和成本效益。该项目旨在开发一个先进的人工智能（AI）驱动的指导系统，该系统将改变诉讼保险索赔的管理和解决方式。该系统通过自动化、有证据支持的指导来加强索赔专业人员的决策过程，将大大减少目前解决索赔所需的时间和费用，从而更快地向索赔人付款，并减少法律的费用负担。该系统的创新方法将有助于确定关键案件信息，支持索赔专业人员做出更明智的决定。该项目有可能提高索赔诉讼程序的总体透明度和可靠性，增强对保险系统的信任。此外，通过精简业务，它可以更有效地利用保险业的资源，降低消费者和企业的保险费。该SBIR第一阶段项目是一个机会，大大改善诉讼保险索赔的处理和处理。该项目的研究目标包括开发一种新的方法，用于从保险索赔中典型的大量非结构化数据集合中提取信息，并提供用于呈现提取信息的摘要框架，从而实现复杂索赔数据的简洁而全面的视图。该项目旨在设计一个可视化界面，以帮助理解和促进索赔专业人员做出更明智的决策。该研究将尖端的人工智能和机器学习技术应用于这些目标，超越了保险行业当前数据分析能力的界限。预期的技术成果包括展示这一创新系统的可行性，以快速准确地从广泛的数据中提供相关的决策信息，为用户提供决策的基本见解。通过改进信息的处理、汇总和呈现方式，该项目预计将在诉讼保险索赔管理中做出更好、更快的决策，为该领域的技术应用制定新标准。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.