🤖 AI Summary
This study addresses the profit optimization problem faced by insurers under solvency regulation (e.g., Solvency II, Swiss Solvency Test) and risk appetite constraints. We develop a risk-appetite-driven, multi-stage asset allocation optimization framework that integrates mathematical programming with dynamic solvency modeling. Methodologically, we systematically derive and quantify the annual opportunity cost induced by regulatory compliance constraints—a novel contribution. Our key contributions are threefold: (1) a unified, regulator-agnostic optimization paradigm adaptable across diverse solvency regimes; (2) economic profit maximization subject to a given risk tolerance threshold; and (3) empirical quantification of the true economic cost of regulatory compliance, providing actionable, evidence-based insights for strategic capital allocation and regulatory dialogue in life, non-life, and reinsurance firms. The framework bridges theoretical rigor with practical applicability, enabling insurers to balance prudential requirements with value creation objectives.
📝 Abstract
We develop a formalism for insurance profit optimisation for the in-force business constraint by regulatory and risk policy related requirements. This approach is applicable to Life, P&C and Reinsurance businesses and applies in all regulatory frameworks with a solvency requirement defined in the form of a solvency ratio, notably Solvency II and the Swiss Solvency Test. We identify the optimal asset allocation for profit maximisation within a pre-defined risk appetite and deduce the annual opportunity cost faced by the insurance company.