AI-Assisted Cognitive-Behavioral Decision Support for Insurance Coverage Selection

Abstract

Selecting appropriate insurance coverage requires evaluating uncertain risks while regulating cognitive and motivational processes. This paper presents ED2® Insurance Choice, an AI-assisted cognitive-behavioral decision-support system designed to help users select a level of insurance coverage that sufficiently reduces financial risk relative to their risk profile. Unlike conventional insurance comparison tools that primarily prioritize premium price, the system focuses on identifying an appropriate extent of coverage (Basic, Standard, or Comprehensive) before selecting an insurance provider. This approach is based on a self-regulation model of problem-solving in which cognitive evaluation of risk reduction and motivational regulation jointly guide the search for a satisficing solution. Integration with Microsoft Azure OpenAI APIs enables the generation of personalized cognitive and motivational risk outcomes, with associated confidence levels, along with solutions for mitigating anticipated difficulties and strategies to support goal attainment. An illustrative example of auto insurance demonstrates how cognitive risk reduction and self-efficacy–driven motivation influence which coverage alternative reaches the satisficing level. The results suggest that AI-supported guidance can improve the transparency of risk evaluation and support more informed insurance coverage selection under uncertainty.

Keywords: AI-assisted Decision Support, Insurance Coverage Selection, Self-regulation, Satisficing, Self-efficacy, Risk Reduction, Bounded Rationality.

DOI: 10.54941/ahfe1007390