Loss Ratio Optimization using Data-Driven Portfolio Segmentation
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P107Keywords:
Loss Ratio, Portfolio Segmentation, Data-Driven Decision Making, Machine Learning, Insurance Analytics, DevSecOps, Cloud Security, Risk OptimizationAbstract
The growing complexity of the risk assessment and underwriting industry in insurance and cloud-enabled financial industries helps push towards more sophisticated methods of optimal loss ratio. Actuarial methods used in a traditional context may be effective within structured forms. Still, they may not maintain the complexity, multi-measure, and dynamically driven risk contexts presented by digital transformation, DevSecOps implementations, and cloud security considerations. This paper suggests an innovative data-driven portfolio segmentation approach to optimize loss ratios using statistical modeling, machine learning algorithms and cloud native and secure architecture. The solution focuses on the automated, scalable, and secure treatment of sensitive information, aligning with the DevSecOps concept. By providing a thorough analysis of historical claims and pivoting off factors like behavior and exposed-to, the study shows how sophisticated segmentation can allow insurers and cloud-based financial institutions to reduce adverse selection, identify unusual claims patterns, and optimize risk-adjusted prices. Results find considerable enhancement of the ratio between premium adequacy and claim liabilities, with loss ratios being optimized by over 15 percent at the controlled simulations. Furthermore, the methodology adheres to contemporary standards of cloud security, ensuring privacy, integrity, and availability in high-assurance environments. This combined use of intelligence-based patterns with DevSecOps-based cloud protection systems offers an avenue through which insurance operations can operate securely and remain efficient over a long-term and sustainable basis. The results are relevant to academia and industry alike, providing a guide towards how insurers can adopt secure, data-based innovation in risk and loss management
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