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Legal Framework For AI-Assisted Claims In Different Jurisdictions




Shivangi Gaur, LLM, SRM University, Sonipat


ABSTRACT


The global insurance industry is witnessing a significant transformation with the widespread adoption of Artificial Intelligence (AI), particularly in claims processing. Insurers worldwide are utilizing AI to enhance efficiency, accuracy, and customer experiences. Key AI applications include underwriting, claims processing, customer service, fraud detection, and risk assessment. Automation of claims intake and triaging, AI-driven claims processing, and risk assessment are prominent trends. However, the importance of comprehensive legal frameworks for AI-assisted claims on a global scale is emphasized. These frameworks protect consumer rights, mitigate risks, address ethical considerations, ensure compliance, and promote international collaboration. The text further delves into specific legal considerations and challenges in the United States, European Union, and Singapore, emphasizing the role of existing laws and regulations in governing AI usage in insurance claims. Various legal frameworks, including the California Consumer Privacy Act(CCPA), General Data Protection Regulation (GDPR), and Personal Data Protection Act (PDPA), are discussed in the context of ensuring fairness, transparency, and compliance. The evolving legal landscape is crucial for insurers to navigate responsibly and ethically as AI continues to advance in the insurance sector.

Indian Journal of Law and Legal Research

Abbreviation: IJLLR

ISSN: 2582-8878

Website: www.ijllr.com

Accessibility: Open Access

License: Creative Commons 4.0

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Licensing: 

 

All research articles published in The Indian Journal of Law and Legal Research are fully open access. i.e. immediately freely available to read, download and share. Articles are published under the terms of a Creative Commons license which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

 

Disclaimer:

The opinions expressed in this publication are those of the authors. They do not purport to reflect the opinions or views of the IJLLR or its members. The designations employed in this publication and the presentation of material therein do not imply the expression of any opinion whatsoever on the part of the IJLLR.

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