End-to-end automation in insurance claims: A guidewire-integrated AI framework for intelligent processing
Quality Engineering Associate Manager, Accenture, Aubrey, Texas.
Research Article
World Journal of Advanced Research and Reviews, 2025, 22(03), 2295-2310
Publication history:
Received on 24 April 2024 revised on 22 June 2024; accepted on 29 June 2024
Abstract:
The insurance industry faces increasing pressure to streamline claims operations, reduce manual effort, and enhance customer satisfaction through faster, intelligent decision-making. This paper presents a scalable, AI-driven framework for zero-touch claims processing, seamlessly integrated with industry platforms such as Guidewire. By combining supervised and unsupervised machine learning, real-time data ingestion, natural language processing, and computer vision, the framework automates core components of claims handling while supporting regulatory compliance and ethical governance.
Deployed across multiple insurance carriers, the solution demonstrated significant operational improvements, including a 50% reduction in manual intervention, accelerated claims resolution times, and enhanced decision accuracy. Guidewire serves as the orchestration layer, enabling seamless integration of predictive analytics into claims workflows, while AI models operate on both structured and unstructured inputs such as claim forms, images, and adjuster notes. Key contributions of this research include a modular architecture for intelligent automation, continuous learning mechanisms for adapting to evolving risk patterns, and interpretable AI components that support auditability. The study provides a practical implementation roadmap for insurers seeking to modernize claims operations through advanced data-driven techniques while maintaining trust, compliance, and operational efficiency.
Keywords:
Artificial Intelligence; Machine Learning; Insurance Fraud Detection; Claims Processing; Predictive Analytics; Anomaly Detection
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0