A Two-Stage AI-Based Framework for Determining Insurance Broker Commissions in the Healthcare Industry

Abstract

Insurance brokers play a critical role in connecting consumers and employers with health insurance plans in the United States, and their compensation in the form of commissions significantly influences the dynamics of the healthcare insurance market. This paper proposes a conceptual two-stage artificial intelligence (AI) driven framework for determining insurance broker commissions in the U.S. healthcare industry. The framework is designed to account for multiple variables that are often overlooked by traditional commission structures, including the size of the insurance policy, the risk profile of the client, the characteristics of the healthcare plan, and the historical performance of the broker. In the first stage of the framework, a data-driven model analyzes policy-specific factors to compute a baseline commission recommendation. In the second stage, a subsequent AI model refines this commission by incorporating broker-specific performance metrics, thereby personalizing the compensation to align with the broker’s track record and value delivery. This two-stage approach allows for a modular and comprehensive analysis that mirrors real-world commission practices (such as base commissions combined with performance-based bonuses), but it is enhanced through AI to achieve greater precision and adaptability. The paper details the design of each stage and the interaction between them, provides a mathematical representation of the framework, and discusses how the model can handle complex variables inherent to the healthcare insurance commission process. Although presented conceptually without empirical case studies, the proposed framework offers a blueprint for leveraging AI in commission determination, aiming to improve incentive alignment and efficiency in the broker-mediated health insurance marketplace.