Mr A Neural Network-Based Study on the Significance of Risk Aversion Bias in Life Insurance Selection.

Abstract

Risk aversion bias, a significant behavioural factor, often causes individuals to make financial choices that diverge from optimal decision-making strategies. The present study is to examine the impact of risk aversion bias on insurance selection decisions by leveraging the predictive power of neural networks. The research utilizes a dataset that includes demographics, latent variables of risk aversion bias, and preference-based variables (binary). It employs a neural network model to assess the extent to which risk aversion bias influences insurance product selection. The study also used bootstrapping analysis to validate the application of the neural network model in small-sample research. The methodology encompasses data preprocessing, model training, and validation to ensure the robustness of the results. Results show evidence that risk aversion bias acts as a major influencing factor in shaping the insurance selection decision, with notable variations across demographic groups. Moreover, when compared to traditional methods, the neural network approach shows superior performance in statistical models and has the capacity to effectively capture the complex, non-linear relationships within behavioural data. Findings from this analysis add meaningful value to the areas of behavioural finance in general and insurance decision-making in particular, providing actionable guidance for insurance providers to tailor their products and strategies in line with consumer biases. The study also highlights neural network potential to improve understanding of the behaviour of life insurance consumer and proposes future research to broaden the model's applicability.