Use of Bayesian Inference in Predictive Modeling for Insurance Claims in India
DOI:
https://doi.org/10.47672/ajsas.2342Keywords:
Bayesian Inference, Predictive Modeling, InsuranceAbstract
Purpose: The aim of the study was to assess the use of bayesian inference in predictive modeling for insurance claims in India.
Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
Findings: The study indicated that Bayesian models outperform traditional frequentist approaches, particularly in dealing with the inherent uncertainties and variability in insurance data. These models can handle small sample sizes more effectively and provide probabilistic predictions, offering a clear advantage in risk assessment and decision-making processes. Additionally, Bayesian inference supports the development of more personalized and granular predictions, accounting for individual policyholder characteristics and behaviors. This leads to more precise premium pricing and better risk management, ultimately improving the financial stability and competitiveness of insurance companies. Overall, the use of Bayesian inference in predictive modeling for insurance claims represents a significant advancement in actuarial science, contributing to more accurate, reliable, and insightful predictive analytics in the insurance industry.
Implications to Theory, Practice and Policy: Bayesian decision theory, information theory and risk theory may be used to anchor future studies on assessing the use of bayesian inference in predictive modeling for insurance claims in India. Insurance companies should adopt Bayesian predictive models to improve the accuracy of risk assessments, premium pricing, and claims management. Policymakers should develop and promote regulatory frameworks that encourage the adoption of advanced predictive modeling techniques, including Bayesian inference, within the insurance industry.
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