Measuring and Mitigating Biases in Motor Insurance Pricing
- URL: http://arxiv.org/abs/2311.11900v2
- Date: Thu, 20 Jun 2024 10:14:12 GMT
- Title: Measuring and Mitigating Biases in Motor Insurance Pricing
- Authors: Mulah Moriah, Franck Vermet, Arthur Charpentier,
- Abstract summary: The non-life insurance sector operates within a highly competitive and tightly regulated framework.
Age-based premium fairness is also mandated in certain insurance domains.
In certain insurance domains, variables such as the presence of serious illnesses or disabilities are emerging as new dimensions for evaluating fairness.
- Score: 1.2289361708127877
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The non-life insurance sector operates within a highly competitive and tightly regulated framework, confronting a pivotal juncture in the formulation of pricing strategies. Insurers are compelled to harness a range of statistical methodologies and available data to construct optimal pricing structures that align with the overarching corporate strategy while accommodating the dynamics of market competition. Given the fundamental societal role played by insurance, premium rates are subject to rigorous scrutiny by regulatory authorities. These rates must conform to principles of transparency, explainability, and ethical considerations. Consequently, the act of pricing transcends mere statistical calculations and carries the weight of strategic and societal factors. These multifaceted concerns may drive insurers to establish equitable premiums, taking into account various variables. For instance, regulations mandate the provision of equitable premiums, considering factors such as policyholder gender or mutualist group dynamics in accordance with respective corporate strategies. Age-based premium fairness is also mandated. In certain insurance domains, variables such as the presence of serious illnesses or disabilities are emerging as new dimensions for evaluating fairness. Regardless of the motivating factor prompting an insurer to adopt fairer pricing strategies for a specific variable, the insurer must possess the capability to define, measure, and ultimately mitigate any ethical biases inherent in its pricing practices while upholding standards of consistency and performance. This study seeks to provide a comprehensive set of tools for these endeavors and assess their effectiveness through practical application in the context of automobile insurance.
Related papers
- 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) - Evaluating COVID-19 vaccine allocation policies using Bayesian $m$-top
exploration [53.122045119395594]
We present a novel technique for evaluating vaccine allocation strategies using a multi-armed bandit framework.
$m$-top exploration allows the algorithm to learn $m$ policies for which it expects the highest utility.
We consider the Belgian COVID-19 epidemic using the individual-based model STRIDE, where we learn a set of vaccination policies.
arXiv Detail & Related papers (2023-01-30T12:22:30Z) - Proportional Fairness in Obnoxious Facility Location [70.64736616610202]
We propose a hierarchy of distance-based proportional fairness concepts for the problem.
We consider deterministic and randomized mechanisms, and compute tight bounds on the price of proportional fairness.
We prove existence results for two extensions to our model.
arXiv Detail & Related papers (2023-01-11T07:30:35Z) - Principal-Agent Hypothesis Testing [54.154244569974864]
We consider the relationship between a regulator (the principal) and an experimenter (the agent) such as a pharmaceutical company.
The efficacy of the drug is not known to the regulator, so the pharmaceutical company must run a costly trial to prove efficacy to the regulator.
We show how to design protocols that are robust to an agent's strategic actions, and derive the optimal protocol in the presence of strategic entrants.
arXiv Detail & Related papers (2022-05-13T17:59:23Z) - Towards a multi-stakeholder value-based assessment framework for
algorithmic systems [76.79703106646967]
We develop a value-based assessment framework that visualizes closeness and tensions between values.
We give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.
arXiv Detail & Related papers (2022-05-09T19:28:32Z) - A Fair Pricing Model via Adversarial Learning [3.983383967538961]
At the core of insurance business lies classification between risky and non-risky insureds.
The distinction between a fair actuarial classification and "discrimination" is subtle.
We show that debiasing the predictor alone may be insufficient to maintain adequate accuracy.
arXiv Detail & Related papers (2022-02-24T10:42:20Z) - Pricing Algorithmic Insurance [3.705785916791345]
We introduce the concept of algorithmic insurance and present a quantitative framework to enable the pricing of the derived insurance contracts.
Our approach outlines how properties of the model, such as accuracy, interpretability and generalizability, can influence the insurance contract evaluation.
arXiv Detail & Related papers (2021-06-01T22:32:02Z) - Post-Contextual-Bandit Inference [57.88785630755165]
Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking.
They can both improve outcomes for study participants and increase the chance of identifying good or even best policies.
To support credible inference on novel interventions at the end of the study, we still want to construct valid confidence intervals on average treatment effects, subgroup effects, or value of new policies.
arXiv Detail & Related papers (2021-06-01T12:01:51Z) - Identifying Undercompensated Groups Defined By Multiple Attributes in
Risk Adjustment [0.09137554315375918]
We evaluate the risk adjustment formulas used in the U.S. health insurance Marketplaces and Medicare.
We find that groups with multiple chronic conditions are undercompensated in the Marketplaces risk adjustment formula.
No complex groups were found to be consistently under- or overcompensated in the Medicare risk adjustment formula.
arXiv Detail & Related papers (2021-05-18T13:09:13Z) - Customer Price Sensitivities in Competitive Automobile Insurance Markets [0.0]
Insurers are increasingly adopting more demand-based strategies to incorporate the indirect effect of premium changes on policyholders' willingness to stay.
We consider a causal inference approach in this paper to account for customer price sensitivities and to deduce optimal, multi-period profit maximizing premium renewal offers.
arXiv Detail & Related papers (2021-01-21T11:07:20Z) - Learning Strategies in Decentralized Matching Markets under Uncertain
Preferences [91.3755431537592]
We study the problem of decision-making in the setting of a scarcity of shared resources when the preferences of agents are unknown a priori.
Our approach is based on the representation of preferences in a reproducing kernel Hilbert space.
We derive optimal strategies that maximize agents' expected payoffs.
arXiv Detail & Related papers (2020-10-29T03:08:22Z)
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.