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X-WR-CALDESC:Events for CREST
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DTSTART:20250330T010000
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DTSTART;TZID=Europe/Helsinki:20250513T100000
DTEND;TZID=Europe/Helsinki:20250520T230000
DTSTAMP:20260710T064230
CREATED:20250506T063233Z
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SUMMARY:François HU (Milliman)  "Obtaining Fair Insurance Premiums with Multiple Sensitive Attributes"
DESCRIPTION:Finance-Insurance\nTime: 10.00 am\nDate:13th of May  2025\nRoom 3001 \nFrançois HU (Milliman) “Obtaining Fair Insurance Premiums with Multiple Sensitive Attributes” \nAbstract : In the context of Algorithmic Fairness\, the goal is to ensure that sensitive attributes have no influence on decision-making outcomes. This objective has driven the development of various fairness definitions and tools\, which have been successfully applied across multiple domains\, including insurance. However\, the challenge becomes more complex when dealing with multiple sensitive attributes. In the absence of intentional discrimination\, predictive models used in insurance – for tasks such as pricing\, fraud detection\, or claims estimation – can inadvertently result in biased decisions\, such as ageism\, racism\, or sexism. As emphasized by Kearns et al. (2019)\, machine learning models do not inherently provide fairness unless explicitly designed to do so. To address this challenge\, we propose a novel sequential framework that progressively achieves fairness across multiple sensitive attributes in insurance applications. By leveraging multi-marginal Wasserstein barycenters\, we extend the concept of Strong Demographic Parity to ensure independence between multiple sensitive features and decision outcomes. Our approach provides a closed-form solution for an optimal\, sequentially fair predictor\, while maintaining interpretability in terms of how the sensitive features interact. Additionally\, our framework accommodates the trade-offs between fairness and risk\, enabling targeted prioritization of fairness improvements for specific attributes based on context-specific requirements. We demonstrate the practical applicability of our framework through a data-driven estimation procedure applied to both synthetic and real-world datasets\, including an insurance dataset. Our numerical experiments show that this post-processing method not only enhances fairness in decision-making but also ensures transparency and maintains statistical guarantees\, offering a robust solution for achieving fairer insurance premiums.\n \nOrganizers:  Jean-David FERMANIAN \n  \n
URL:https://crest.science/event/francois-hu-milliman-obtaining-fair-insurance-premiums-with-multiple-sensitive-attributes/
CATEGORIES:Finance-Insurance,Seminars
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DTSTART;TZID=Europe/Helsinki:20250513T110000
DTEND;TZID=Europe/Helsinki:20250513T121500
DTSTAMP:20260710T064230
CREATED:20241104T074112Z
LAST-MODIFIED:20241104T074126Z
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SUMMARY:Robert Miller (Carnegie Mellon University)
DESCRIPTION:Organized by ENSAI’s Economics Department\, enable guest researchers to present their work. They are open to the public and subject to registration. \nMay 13\, 2025\, 11am-12.15pm: Robert Miller (Carnegie Mellon University) \n
URL:https://crest.science/event/robert-miller-carnegie-mellon-university/
CATEGORIES:Economics,Seminars
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BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20250513T121500
DTEND;TZID=Europe/Helsinki:20250513T133000
DTSTAMP:20260710T064230
CREATED:20250401T074153Z
LAST-MODIFIED:20250507T125728Z
UID:18013-1747138500-1747143000@crest.science
SUMMARY:Stefano CARIA (University of Warwick) - "Mitigating the Consequences of Job Loss: Experimental Evidence After a Tariff Shock"
DESCRIPTION:Applied Micro Seminar : Every Tuesday \nTime: 12:15 pm – 13:30 pm\nDate: 13th of May\nRoom : 3001 \n  \nStefano CARIA (University of Warwick) – “Mitigating the Consequences of Job Loss: Experimental Evidence After a Tariff Shock” joint work with Lukas Hensel\, Girum Abebe and Francois Gerard. \n  \nAbstract : “Job loss is an understudied risk for formal workers in low-income countries\, and it is unclear how to optimally insure workers against it. We provide evidence on the impacts of job loss among female factory workers in Ethiopia and on how these impacts can be mitigated. We leverage quasi-experimental variation in job loss\, experimental variation in job-loss support payments\, and high-frequency data spanning a period of 13 months after displacement. We find that job loss is a persistent shock that reduces employment and consumption spending for longer than one year\, and almost doubles the rate of poverty. An additional lump-sum payment encourages early spending and reduces both overall and manufacturing employment. In contrast\, providing an equivalent amount in monthly tranches — a payment modality preferred by a majority of workers — enables workers to better smooth consumption expenditures without negative employment effects. We show that workers have high willingness to pay for additional job-loss insurance\, but also heterogeneous preferences over the payment modality. This generates a key trade-off between workers’ private welfare and the government industrialization objectives: allowing workers to choose their preferred insurance product maximises their surplus\, but at the cost of reducing future manufacturing employment”. \n  \n  \nOrganizers:\nBenoît SCHMUTZ (Pôle économie du CREST)\nClément MALGOUYRES (Pôle économie du CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/https-warwick-ac-uk-fac-soc-economics-staff-ascaria/
CATEGORIES:Applied Seminar,Seminars
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DTSTART;TZID=Europe/Helsinki:20250513T130000
DTEND;TZID=Europe/Helsinki:20250513T161500
DTSTAMP:20260710T064230
CREATED:20250312T140222Z
LAST-MODIFIED:20250505T070355Z
UID:17951-1747141200-1747152900@crest.science
SUMMARY:Jan Obloj (University of Oxford): "X-OT: On Variants Of Optimal Transport Problem and Understanding Model Robustness"
DESCRIPTION:Jan Obloj (University of Oxford): “X-OT: On Variants Of Optimal Transport Problem and Understanding Model Robustness” \n06/05/2025 – 07/05/2025 – 13/05/2025 – 14/05/2025 \nRoom 2043 \nReferent: Peter Tankov \n
URL:https://crest.science/event/jan-obloj-university-of-oxford-x-ot-on-variants-of-optimal-transport-problem-and-understanding-model-robustness-2/
CATEGORIES:Doctoral Courses,Finance
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BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20250513T160000
DTEND;TZID=Europe/Helsinki:20250520T170000
DTSTAMP:20260710T064230
CREATED:20250506T064610Z
LAST-MODIFIED:20250506T064610Z
UID:18061-1747152000-1747760400@crest.science
SUMMARY:Antonio OCELLO (Ecole Polytechnique)  "Convergence Analysis of Diffusion Models: Towards Reliable Sampling"
DESCRIPTION:Finance-Insurance\nTime: 4.00 p.m.\nDate:13th of May  2025\nRoom 3001 \nAntonio OCELLO (Ecole Polytechnique) “Convergence Analysis of Diffusion Models: Towards Reliable Sampling” \nAbstract : Generative models are increasingly explored in insurance for tasks such as risk simulation\, scenario generation\, and synthetic data augmentation. Their usefulness hinges on the ability to reproduce stylized features of actuarial and claims data—such as heavy tails\, skewed marginals\, and rare event structures—essential for solvency analysis and pricing under uncertainty. Among the available methods\, Score-Based Generative Models (SGMs)\, also known as diffusion models\, offer a flexible framework to sample from complex\, high-dimensional distributions. However\, a key challenge lies in rigorously understanding their convergence.\nIn this talk\, I will present recent advances in the theoretical analysis of SGMs\, focusing on convergence guarantees relevant for actuarial applications. First\, I will show how the choice of the noise schedule impacts generative performance\, and provide explicit bounds on KL divergence and Wasserstein-2 distance. Second\, I will introduce a new convergence analysis in Wasserstein-2 distance\, based on the Ornstein–Uhlenbeck process\, that remains valid beyond log-concave settings—such as for mixtures of Gaussians. Finally\, I will discuss some open problems in the simulation of data for insurance purposes.\nThis talk is based on joint work with Stanislas Strasman\, Claire Boyer\, Sylvain Le Corff\, and Vincent Lemaire (TMLR 2024 – https://openreview.net/forum?id=BlYIPa0Fx1)\, as well as a recent collaboration with Marta Gentiloni-Silveri (ICML 2025 – https://arxiv.org/pdf/2501.02298).\n\n \nOrganizers:  Jean-David FERMANIAN \n  \n
URL:https://crest.science/event/antonio-ocello-ecole-polytechnique-convergence-analysis-of-diffusion-models-towards-reliable-sampling/
CATEGORIES:Finance-Insurance,Seminars
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