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Research Paper | Information Technology | United States of America | Volume 13 Issue 12, December 2024 | Popularity: 4.6 / 10

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Autonomous Claims Processing: Building Self-Driving Workflows with Gen AI and ML in Guidewire

Sateesh Reddy Adavelli

Abstract: Generative artificial intelligence (Gen AI) and machine learning (ML) technologies are changing the way the insurance industry looks, particularly by integrating Gen AI & ML technologies. As a leading platform for property and casualty insurance, Guidewire provides a perfect platform for deploying intelligent claims processing workflows that can dramatically improve efficiency, accuracy and customer satisfaction. The seamless integration of Gen AI and ML capabilities into Guidewire to autonomously process claims is explored in this paper. Insurers can use advanced models to drive self-driving workflows that are capable of automating tasks like claims triage, fraud detection, damage assessment and settlement optimization. But most importantly, these technologies reduce operational costs, and reduce the human intervention involved such that claims can be handled faster and with more accuracy. In this work, we discuss what technical and strategic considerations need to be taken into account when implementing such workflows, such as data integration, model training, and ethical AI practices. This paper also demonstrates several real-world use cases, the challenges you in each, and the potential to scale these solutions across lines of business. With an eye on the future, autonomous claims processing enables insurers to find their next competitive edge by providing innovative solutions that alter the course of expectations of customers while qualifying to regulatory and compliance standards. Self-driving workflows powered by Gen AI and ML in claims are the future of insurance and their ability to transform the entire process of claims management.

Keywords: Autonomous claims processing, Guidewire, Generative AI, Machine learning, Self-Driving workflows, Claims automation, Fraud detection, Damage assessment

Edition: Volume 13 Issue 12, December 2024

Pages: 1348 - 1357

DOI: https://www.doi.org/10.21275/SR241221052213

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