Reimagining Commercial Insurance with AI: Intelligent Risk Assessment, Dynamic Pricing, and Predictive Claims Management

Authors

  • Sukruthi Reddy Sangannagari Senior Quality Assurance Specialist and Full Stack Developer, FM Global, USA Author

DOI:

https://doi.org/10.15662/wdj4rf44

Keywords:

Artificial Intelligence, Commercial Insurance, Intelligent Underwriting, Dynamic Pricing, Predictive Claims Analytics, Telematics, Internet of Things (IoT), Explainable AI, Insurance Analytics, Regulatory Compliance

Abstract

The commercial insurance industry is at a critical inflection point as traditional underwriting, pricing, and claims management models struggle to keep pace with the complexity, speed, and volatility of modern risk environments. Rapid advances in Artificial Intelligence (AI), machine learning, cloud computing, and Internet of Things (IoT) technologies are fundamentally reshaping how insurers assess risk, price policies, and manage claims across large-scale commercial portfolios. This paper presents a comprehensive technical exploration of how AI-driven architectures enable intelligent risk assessment, dynamic pricing, and predictive claims management in commercial insurance ecosystems. It examines end-to-end AI pipelines spanning real-time data ingestion, feature engineering, model training, explainable AI (XAI), and regulatory-compliant deployment. The study further integrates real-world industry case studies, including Zurich’s IoT-enabled risk scoring, Progressive’s telematics-driven pricing, and Lemonade’s AI-based claims automation. Regulatory alignment with the NAIC AI Governance Framework, the NIST AI Risk Management Framework, and the EU AI Act is also addressed. The paper demonstrates that AI-powered insurance platforms can significantly enhance underwriting precision, reduce claims settlement time, improve fraud detection, and enable continuous, behavior-driven premium optimization, positioning AI as the foundational engine of next-generation commercial insurance.

References

1. National Institute of Standards and Technology, “AI Risk Management Framework (AI RMF 1.0),” NIST, 2023.

2. National Association of Insurance Commissioners, “Model Bulletin on the Use of Artificial Intelligence Systems by Insurers,” NAIC, 2023.

3. S. Bhattacharya et al., “Predictive Analytics in Insurance Risk Management,” IEEE Access, vol. 10, 2022.

4. McKinsey & Company, “The Future of Insurance: AI-Driven Business Models,” 2023..

5. J. Wüthrich and M. Merz, Statistical Foundations of Actuarial Learning and Its Applications, Springer, 2021.

Relevance: Core reference on machine learning methods for insurance risk assessment, pricing models, and predictive analytics.

6. McKinsey & Company, “Insurance 2030—The impact of AI on the future of insurance,” McKinsey Global Institute, 2021.

Relevance: Discusses AI-driven underwriting, intelligent risk evaluation, dynamic pricing strategies, and claims automation in commercial insurance.

7. K. G. Srinivasan, R. K. Singh, and P. K. Sharma, “Machine learning techniques for risk assessment and claim prediction in insurance,” IEEE Access, vol. 9, pp. 112345–112357, 2021.

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Published

2024-01-10

How to Cite

Reimagining Commercial Insurance with AI: Intelligent Risk Assessment, Dynamic Pricing, and Predictive Claims Management. (2024). International Journal of Advanced Research in Computer Science & Technology(IJARCST), 7(1), 9700-9711. https://doi.org/10.15662/wdj4rf44