AI-Augmented Conceptual Model of Compromise Interest Balancing Among Insurance Market Parties

Abstract

An AI-augmented conceptual model of compromise interest balancing among insurance market parties (the insurance agent, the client, and the regulator) is proposed, based on Quality Function Deployment (QFD) methodology. In the model, the agent's goals (commission rate, number of transactions, low chargeback rate, licence security) serve as WHATs requirements, while the mechanisms for their achievement (HOWs) are represented by two groups of means: satisfying client needs and complying with regulatory standards. A five-stage AI-QFD model is developed with clearly defined integration points for four artificial intelligence tools. The model enables objective formation of the "requirements–actions" matrix based on real data, replacing expert assumptions with measurable indicators and achieving a balance among agent profitability, client satisfaction, and regulatory compliance. It is demonstrated how the AI layer transforms the classical two-dimensional QFD matrix into a three-dimensional decision-support instrument for investment and insurance decisions.