OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a Gradient Based Learning

arXiv:2404.10275v1 Announce Type: new
Abstract: This paper presents a novel approach to optimizing profit margins in non-life insurance markets through a gradient descent-based method, targeting three key objectives: 1) maximizing profit margins, 2) ensuring conversion rates, and 3) enforcing fairness criteria such as demographic parity (DP). Traditional pricing optimization, which heavily lean on linear and semi definite programming, encounter challenges in balancing profitability and fairness. These challenges become especially pronounced in situations that necessitate continuous rate adjustments and the incorporation …

abstract arxiv conversion conversion rates cs.ai cs.cy cs.lg elasticity fair fairness gradient insurance key life margins markets novel optimization paper price profit profit margins stat.ap targeting through type via