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

• URL: http://arxiv.org/abs/2404.10275v1
• Date: Tue, 16 Apr 2024 04:21:59 GMT
• Title: OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a Gradient Based Learning
• Authors: Vincent Grari, Marcin Detyniecki,
• Abstract summary: This paper presents a novel approach to optimizing profit margins in non-life insurance markets through a gradient descent-based method. It targets three key objectives: 1) maximizing profit margins, 2) ensuring conversion rates, and 3) enforcing fairness criteria such as demographic parity (DP)
• Score: 7.145413681946911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer [52.09480867526656]
  We identify the source of misalignment as a form of distributional shift and uncertainty in learning human preferences. To mitigate overoptimization, we first propose a theoretical algorithm that chooses the best policy for an adversarially chosen reward model. Using the equivalence between reward models and the corresponding optimal policy, the algorithm features a simple objective that combines a preference optimization loss and a supervised learning loss.
  arXiv  Detail & Related papers  (2024-05-26T05:38:50Z)
• $i$REPO: $i$mplicit Reward Pairwise Difference based Empirical Preference Optimization [12.266207199002604]
  Large Language Models (LLM) can sometimes produce outputs that deviate from human expectations. We propose a novel framework named $i$REPO, which utilizes implicit Reward pairwise difference regression for Empirical Preference Optimization. We show that $i$REPO effectively achieves self-alignment using soft-label, self-generated responses and the logit of empirical AI annotators.
  arXiv  Detail & Related papers  (2024-05-24T05:42:11Z)
• Rethinking and Benchmarking Predict-then-Optimize Paradigm for Combinatorial Optimization Problems [62.25108152764568]
  Many web applications rely on solving optimization problems, such as energy cost-aware scheduling, budget allocation on web advertising, and graph matching on social networks. We consider the performance of prediction and decision-making in a unified system. We provide a comprehensive categorization of current approaches and integrate existing experimental scenarios.
  arXiv  Detail & Related papers  (2023-11-13T13:19:34Z)
• A predict-and-optimize approach to profit-driven churn prevention [1.03590082373586]
  We frame the task of targeting customers for a retention campaign as a regret minimization problem. Our proposed model aligns with the guidelines of Predict-and-optimize (PnO) frameworks and can be efficiently solved using gradient descent methods. Results underscore the effectiveness of our approach, which achieves the best average performance compared to other well-established strategies in terms of average profit.
  arXiv  Detail & Related papers  (2023-10-10T22:21:16Z)
• Insurance pricing on price comparison websites via reinforcement learning [7.023335262537794]
  This paper introduces reinforcement learning framework that learns optimal pricing policy by integrating model-based and model-free methods. The paper also highlights the importance of evaluating pricing policies using an offline dataset in a consistent fashion.
  arXiv  Detail & Related papers  (2023-08-14T04:44:56Z)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints [51.12047280149546]
  A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints. We formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. We demonstrate the effectiveness of our approach on real-world data under different fairness metrics.
  arXiv  Detail & Related papers  (2022-12-23T22:29:08Z)
• Universal Trading for Order Execution with Oracle Policy Distillation [99.57416828489568]
  We propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. We show that our framework can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information.
  arXiv  Detail & Related papers  (2021-01-28T05:52:18Z)
• Fast Rates for Contextual Linear Optimization [52.39202699484225]
  We show that a naive plug-in approach achieves regret convergence rates that are significantly faster than methods that directly optimize downstream decision performance. Our results are overall positive for practice: predictive models are easy and fast to train using existing tools, simple to interpret, and, as we show, lead to decisions that perform very well.
  arXiv  Detail & Related papers  (2020-11-05T18:43:59Z)
• Online Regularization towards Always-Valid High-Dimensional Dynamic Pricing [19.11333865618553]
  We propose a novel approach for designing dynamic pricing policy based regularized online statistical learning with theoretical guarantees. Our proposed online regularization scheme equips the proposed optimistic online regularized maximum likelihood pricing (OORMLP) pricing policy with three major advantages. In theory, the proposed OORMLP algorithm exploits the sparsity structure of high-dimensional models and secures a logarithmic regret in a decision horizon.
  arXiv  Detail & Related papers  (2020-07-05T23:52:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.