This research proposes Dynamic Discrimination-Insensitive Pricing (DDIP), a novel framework for fair insurance pricing that addresses temporal dynamics in portfolios. The approach combines causal risk assessment, dynamic fairness constraints, and portfolio-level optimization to balance profitability with non-discrimination. It formalizes pricing as a constrained sequential decision-making process with theoretical guarantees for the fairness-accuracy trade-off under evolving market conditions.
Key findings
DDIP integrates causal fairness mechanisms with dynamic portfolio optimization to prevent discrimination while maintaining profitability
The framework employs a three-module architecture: causal risk assessment engine, dynamic fairness constraint layer, and portfolio-level optimization mechanism
Theoretical results characterize the fairness-accuracy Pareto frontier under dynamic conditions with temporal uncertainty
Addresses critical gaps including temporal dynamics, concept drift, and strategic consumer behavior in insurance markets
Identifies implementation risks including regulatory compliance, model drift, and strategic behavior with proposed mitigation strategies
Limitations & open questions
Experimental validation remains pending as this is a research proposal
Framework assumes access to causal structure of risk factors which may be difficult to identify in practice
Implementation faces challenges from evolving regulatory requirements and strategic consumer responses