OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a
  Gradient Based Learning
Vincent Grari Marcin Detyniecki
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 of fairness criteria.
Specifically, indirect Ratebook optimization, a widely-used method for new
business price setting, relies on predictor models such as XGBoost or GLMs/GAMs
to estimate on downstream individually optimized prices. However, this strategy
is prone to sequential errors and struggles to effectively manage optimizations
for continuous rate scenarios. In practice, to save time actuaries frequently
opt for optimization within discrete intervals (e.g., range of [-20\%, +20\%]
with fix increments) leading to approximate estimations. Moreover, to
circumvent infeasible solutions they often use relaxed constraints leading to
suboptimal pricing strategies. The reverse-engineered nature of traditional
models complicates the enforcement of fairness and can lead to biased outcomes.
Our method addresses these challenges by employing a direct optimization
strategy in the continuous space of rates and by embedding fairness through an
adversarial predictor model. This innovation not only reduces sequential errors
and simplifies the complexities found in traditional models but also directly
integrates fairness measures into the commercial premium calculation. We
demonstrate improved margin performance and stronger enforcement of fairness
highlighting the critical need to evolve existing pricing strategies.