AI in Insurance Claims Processing: Balancing Innovation with Implementation Challenges

This article explores the multifaceted implementation of artificial intelligence in insurance claims processing, examining both transformative successes and persistent challenges. It analyzes how AI technologies automate workflows, enhance fraud detection capabilities, improve customer interactions, reduce processing errors, and accelerate claim settlements. The discussion extends to critical implementation barriers, including legacy system integration difficulties, algorithmic bias concerns, resource constraints for smaller insurers, model explainability issues, and regulatory compliance challenges. By providing a balanced technical assessment of current applications alongside practical solutions for common obstacles, this article offers insurance professionals a comprehensive framework for navigating AI adoption decisions while maintaining ethical standards and stakeholder trust.

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Publication Details

Journal
European Journal of Computer Science and Information Technology
Published
2025-04-15
DOI
https://doi.org/10.37745/ejcsit.2013/vol13n275870
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
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article

AI in Insurance Claims Processing: Balancing Innovation with Implementation Challenges

Naresh Babu Goolla
European Journal of Computer Science and Information Technology
Ethics and Social Impacts of AI
article

AI in Insurance Claims Processing: Balancing Innovation with Implementation Challenges

Naresh Babu Goolla
article en

Abstract

This article explores the multifaceted implementation of artificial intelligence in insurance claims processing, examining both transformative successes and persistent challenges. It analyzes how AI technologies automate workflows, enhance fraud detection capabilities, improve customer interactions, reduce processing errors, and accelerate claim settlements. The discussion extends to critical implementation barriers, including legacy system integration difficulties, algorithmic bias concerns, resource constraints for smaller insurers, model explainability issues, and regulatory compliance challenges. By providing a balanced technical assessment of current applications alongside practical solutions for common obstacles, this article offers insurance professionals a comprehensive framework for navigating AI adoption decisions while maintaining ethical standards and stakeholder trust.

European Journal of Computer Science and Information TechnologyVol. 13(27)
European Centre for Soft Computing (ES)
Openalex Percentile: Top 89%
Ethics and Social Impacts of AI
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