A Framework for Incorporating Behavioral Biases into Insurance Risk Models

Thesis Abstract

The increasing recognition of behavioral biases as critical factors influencing individual decision-making has profound implications for the development and accuracy of insurance risk models. Traditional actuarial models predominantly rely on rational choice theory and quantitative data, often neglecting the psychological and cognitive distortions that affect policyholders' risk assessments and behavior, thereby limiting the predictive accuracy and robustness of these models. This study aims to develop an integrated framework for embedding behavioral biases—such as optimism bias, risk aversion, and loss aversion—into existing insurance risk modeling practices to enhance their predictive validity and practical utility. The specific objectives include (1) identifying key behavioral biases affecting insurance decision-making; (2) analyzing the theoretical underpinnings of these biases through relevant behavioral and economic theories, including Prospect Theory and Dual Process Theory; (3) evaluating the extent to which these biases influence risk perception and reporting; and (4) constructing a conceptual framework that incorporates behavioral insights into quantitative risk models. Employing a mixed-methods research design, the study combined qualitative and quantitative approaches. The qualitative component involved thematic analysis of in-depth interviews with 30 insurance industry professionals, including underwriters, actuaries, and risk analysts, to identify prevalent behavioral biases in insurance settings. The quantitative phase used a survey instrument administered to 400 policyholders across diverse demographic groups within a metropolitan region, capturing data on risk perception, decision-making tendencies, and behavioral traits. The survey's psychometric properties were validated through Cronbach’s alpha and exploratory factor analysis to ensure reliability and construct validity. Data analysis employed multiple regression analysis to quantify the influence of identified behavioral biases on insurance risk behaviors, complemented by Structural Equation Modeling (SEM) to test the proposed framework's explanatory power. The study also used model comparison techniques, such as Akaike Information Criterion (AIC), to evaluate the fit of the behavioral-infused risk models against conventional models. The anticipated findings suggest that incorporating behavioral biases significantly improves the predictive accuracy of insurance risk assessments, with particular biases, such as optimism bias and loss aversion, exerting measurable influence on policyholder behaviors including claim reporting and premium payment tendencies. The results are expected to demonstrate that models integrating these biases outperform traditional models in goodness-of-fit metrics and predictive precision, thereby addressing existing gaps in actuarial modeling practices. This research contributes to the theoretical advancement of insurance risk modeling by operationalizing behavioral theories within quantitative frameworks, offering a more nuanced understanding of risk perception and decision-making. It bridges the disciplinary gap between behavioral economics and actuarial science, providing a novel, integrative model that policymakers and industry practitioners can adopt for more accurate risk assessment and pricing strategies. The study concludes with practical recommendations for insurance firms to incorporate behavioral data collection and analysis into their risk management procedures, advocating for the development of bias-sensitive risk models. It also suggests avenues for future research, including the exploration of additional biases and their interactions within diverse market contexts. Overall, the study emphasizes the importance of embracing behavioral insights to refine risk assessment methodologies, ultimately fostering more resilient and customer-centric insurance systems.