A fair pricing model via adversarial learning

Grari, Vincent; Charpentier, Arthur; Lamprier, Sylvain; Detyniecki, Marcin

doi:10.48550/arxiv.2202.12008

Cited by 3 publications

(13 citation statements)

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“…Multi-output neural network regression model. In constructing (and fitting) the plain-vanilla FNN best-estimate prices (7), we directly use the discriminatory information D = d of the policyholders as an input variable to the FNN. Our proposal is to change this FNN architecture such that only the non-discriminatory information X = x is used as an input variable to the network, but at the same time we generate a whole family of best-estimate prices that reflects the different specifications (levels) of the discriminatory information.…”

Section: 2mentioning

confidence: 99%

“…where in the regression function µ(•) = µ θ (•) we highlight its dependence on the parameter θ to be optimized. This is the process to fit the plain-vanilla FNN given in (7), subject to early stopping to prevent from in-sample over-fitting; for a detailed discussion of FNN fitting we refer to Section 7.2.3 in Wüthrich-Merz [15]. For fitting the multi-output FNN (8) we modify this fitting procedure as follows…”

Section: 2mentioning

confidence: 99%

“…• The multi-task FNN ( 16) is solely used to model the discrimination-free insurance price through (5). There are many different notions and definitions of fairness that may complement the discrimination-free insurance prices; see, e.g., Grari et al [7]. Multi-task learning ( 16)-( 17) can be extended by such complementary fairness notions.…”

Section: 3mentioning

confidence: 99%

“…• Alternatively to using certain scores for regularization, one can also setup a generative-adversarial network (GAN), that tries to infer protected information from the prices. A successful implementation of such a GAN architecture will force the pricing network to design a pricing functional from which the gender cannot be inferred, we also refer to Grari et al [7].…”

Section: 3mentioning

confidence: 99%

“…We call the impact of such inference on prices indirect discrimination. We note that this is a narrow use of the latter term, equivalent to what is also known as proxy discrimination, and does not consider any aspects of fairness and disparate impact; we also refer to Prince-Schwarcz [10], Frees-Huang [6], Lindholm et al [9], Xin-Huang [16] and Grari et al [7] for relevant discussions.…”

Section: Introductionmentioning

confidence: 99%

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A multi-task network approach for calculating discrimination-free insurance prices

Lindholm¹,

Richman²,

Tsanakas³

et al. 2022

Preprint

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In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protected policyholder characteristics are implicitly inferred from non-protected ones by predictive models, and are thus having an undesirable (or illegal) impact on prices. A technical solution to this problem relies on building a best-estimate model using all policyholder characteristics (including protected ones) and then averaging out the protected characteristics for calculating individual prices. However, such approaches require full knowledge of policyholders' protected characteristics, which may in itself be problematic. Here, we address this issue by using a multi-task neural network architecture for claim predictions, which can be trained using only partial information on protected characteristics, and it produces prices that are free from proxy discrimination. We demonstrate the use of the proposed model and we find that its predictive accuracy is comparable to a conventional feedforward neural network (on full information). However, this multi-task network has clearly superior performance in the case of partially missing policyholder information.

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Section: 2mentioning

confidence: 99%