Volume 19, Issue 2

Reserve calculation is crucial for insurance companies. Due to the long-term nature of life insurance products, stochastic interest rate models are more suitable when calculating the premium and reserve of a life insurance product. In this article, we use several popular mean-reverting stochastic interest rate models to study the impact of the model and its parameters on the values of reserves for life insurance products. We employ linear regression, moment estimation, and error optimization methods to calibrate the Vasicek, Cox-Ingersoll-Ross (CIR), CIR#, and Chen models. Our analysis reveals that when applying mean-reverting stochastic interest rate models to a whole life insurance policy, the initial interest rate and the long-term mean have a significant influence on the net reserve value. However, the speed of reversion and volatility only marginally impact the net reserve. Additionally, we observe that the numerical result of the net reserve is sensitive to the time period of the interest rate data and the term structures of the yield rates used in the analysis.

Consistent crop information is vital for the survival of the crop insurance sector, which relies on historical crop data, weather records, meteorological information, and farmers’ details. In India, fragmented, low-quality, and costly data have led to adverse claims ratios, forcing insurance companies to exit the market. This paper proposes an integrated framework that assimilates crop details, quality satellite data, and an actuarial model for crop yield estimation. We use kernel density estimation for risk assessment and emphasize the critical role of bandwidth calculation. Our research indicates that traditional heuristics for bandwidth selection can be misleading. A visualization of the fitted distribution with a frequency histogram can often provide tell-tale signs of an erroneous conclusion from the heuristics. We emphasize the role of the modeller’s judgment in determining the optimal bandwidth that is free from overfitting or over-smoothing. The framework bridges the gap between data and the insurer. The proposed model is of regulatory importance as it solves the issue of missing data and improves risk assessment, which will improve crop insurance market penetration and farmers’ participation and thereby promote stability in the crop insurance sector.

Despite the inherent benefits of usage-based auto insurance (UBI), such as loss prevention and reduced premiums, the UBI market has encountered limitations hindering its expansion in practical terms. This study aims to explore the attitudes of consumers towards UBI and the pivotal factors influencing their decision to adopt UBI. According to the survey, a better understanding of the rationale behind pay-as-you-drive auto insurance (PAYD) and pay-how-you-drive auto insurance (PHYD) plays an important role in policyholders’ acceptance. In addition, consumers with higher auto insurance premiums and individuals willing to share their data are more likely to buy UBI. Our research suggests that the promotion of UBI should be aligned with increasing individuals’ willingness to share personal information and reducing privacy concerns. This could not only increase the willingness to purchase UBI but also help balance discount expectations between policyholder and insurer.

This study first examines the dimensions of organizational risk culture as a latent factor, measures its robustness, and assesses its impact on risk management performance through empirical validation in the insurance industry context. Building upon existing literature, a conceptual model is developed with five exogenous factors – “leadership commitment and support,” risk governance,” “risk awareness and communication,” “competency and resources,” and “performance review and rewards’ as antecedents of organizational risk culture and risk management performance as an endogenous factor. Using a descriptive research design and a comprehensive thirty-one-item scale, we collected primary data from Indian general insurance companies to validate the causal model of risk culture using structural equation modeling (SEM). The results indicate that ‘leadership commitment and support’ is the foremost determinant of risk culture, followed by competency and resources, risk governance, and risk awareness and communication. The analysis further reveals that risk culture strongly impacts the risk management performance in the Indian insurance sector. In addition to demonstrating empirical evidence, the study also provides the risk culture assessment tool to risk management practitioners from the insurance industry.