{ "id": 5774, "date": "2025-08-19T05:43:51", "date_gmt": "2025-08-19T05:43:51", "guid": { "rendered": "https:\/\/detralytics.com\/?p=5774" }, "modified": "2026-08-21T13:30:51", "modified_gmt": "2026-08-21T13:30:51", "slug": "in-processing-of-actuarial-and-equity-fairness-constraints-for-neural-networks", "status": "publish", "type": "post", "link": "https:\/\/detralytics.com\/en\/publication\/in-processing-of-actuarial-and-equity-fairness-constraints-for-neural-networks\/", "title": { "rendered": "In-processing of actuarial and equity fairness constraints for Neural networks" }, "content": { "rendered": "
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In-processing of actuarial and equity fairness constraints for neural networks<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Detra Note 2025-4<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This article introduces a novel in-processing method for integrating actuarial and equity fairness into neural networks used for actuarial valuation. We consider one primary network penalized during training to ensure balanced predictions (actuarial fairness) and independence from sensitive features (equity fairness). Global and local actuarial equilibrium is obtained by aligning the inter-quantile averages of predicted and observed responses. Meanwhile, a second auxiliary network penalizes the primary network for discriminatory predictions. The combined training algorithm effectively preserves predictive accuracy while mitigating discrimination. Numerical illustrations on real-world datasets demonstrate the method\u2019s efficacy in achieving fair and reliable insurance pricing models.<\/p><\/div><\/div><\/div>

Keywords: <\/span><\/strong>neural network, equity fairness, actuarial fairness, non-life pricing.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Sector: <\/b>Insurance<\/p>

<\/b>Expertise: <\/b>Non-life pricing<\/p>

Authors: <\/b>Donatien Hainaut<\/p>

Publisher:<\/b> Detralytics<\/p>

Date:<\/b> April 2025<\/p>

Language:<\/b> English<\/p>

Pages:<\/b> 27<\/p>

Reference :<\/b> Detra Note 2025-4<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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About the authors<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Donatien\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Donatien Hainaut<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Donatien Hainaut is a Scientific Advisor at Detralytics and a professor at UCLouvain (Belgium), where he serves as the Director of the Master\u2019s program in Data Science with a statistical orientation. Prior to this, he held several academic positions, including Associate Professor at Rennes School of Business and ENSAE in Paris. He also has extensive industry experience, having worked as a Risk Officer, Quantitative Analyst, and ALM Officer.<\/p>

Donatien is a Qualified Actuary and holds a PhD in the field of Asset and Liability Management. His current research focuses on contagion mechanisms in stochastic processes and the applications of neural networks in insurance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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This paper proposes a new approach to risk classification based on Generalized Gaussian Pro-cess Regression (GGPR).<\/p>", "protected": false }, "author": 1, "featured_media": 5775, "comment_status": "closed", "ping_status": "closed", "sticky": false, "template": "", "format": "standard", "meta": { "footnotes": "" }, "categories": [ 133 ], "tags": [], "class_list": [ "post-5774", "post", "type-post", "status-publish", "format-standard", "has-post-thumbnail", "hentry", "category-publication" ], "yoast_head": "\nDetra Note : Neural networks<\/title>\n<meta name=\"description\" content=\"This article introduces a novel in-processing method for integrating actuarial and equity fairness into neural networks used for actuarial valuation.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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