Enhancing Claims Processing with AI: Boosting Operational Efficiency in P&C Insurance
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P108Keywords:
Artificial Intelligence, Machine Learning, Computer Vision, NLP, Robotic Process Automation, Fraud Detection, Customer ExperienceAbstract
Operational effectiveness and consumerism are forcing insurers to embrace disruptive technologies in the modern dynamic insurance environment for Property and Casualty (P&C) insurance. Artificial Intelligence (AI) is one of them, as it enables claims to be processed faster, more accurately, and at reduced costs. This paper will discuss the application of AI in the claims lifecycle through Machine Learning (ML), Natural Language Processing (NLP), computer vision, and Robotic Process Automation (RPA) to solve the traditional setbacks, including manual data entry, subjective evaluation, delays in settlements, and susceptibility to fraud. Real-life case scenarios since 2022 and before have shown that there is a practical advantage (up to 80% of claims processes can be fastened, better damage assessment, and fraud claims actions are reduced by 25-40%). The paper then describes an end-to-end architecture for AI-driven automation of claims, covering the use of preprocessing pipelines, model deployment, and feedback experiences, as well as integration with legacy insurance systems. The benefits have been enormous, but there are still issues with data quality, model transparency, regulatory alignment, and ethics. Nevertheless, the potential of AI to transform the claims processing process is obvious, and it will not only bring insurers operational efficiencies but also help them gain a competitive advantage through faster, fairer, and more personalised experiences for their customers. The paper concludes with recommendations for strategic implications to scale AI responsibly throughout the insurance industry
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