Category Archives: Research

Risque de sécheresse et de subsidence

Jeudi, en arrivant sur Paris, je donnerai un exposé pour présenter predicting drought and subsidence risks in France, publié dans le numéro spécial Drought vulnerability, risk, and impact assessments: bridging… de NHESS (Nat. Hazards Earth Syst. Sci.), écrit avec Molly James, et Hani Ali, ainsi que le travail sur les inondations, en France, Insurance against natural catastrophes: balancing actuarial fairness and social solidarity. Les slides sont en ligne.

Deutsche Gesellschaft für Versicherungs- und Finanzmathematik

Next week the Convention-A conference will take place, with more than 1000 participants, 200 sessions, and I was invited to give a talk in a Machine Learning session, organized by the Deutsche Gesellschaft für Versicherungs- und Finanzmathematik on Tuesday. It will be based on the revised version of our paper a fair pricing model via adversarial learning. Slides are now available online.

Workshop on Impacts of Climate Change on Economics, Finance, and Insurance

Next week, I will be at the Fields Institute in Toronto, for a workshop on Impacts of Climate Change on Economics, Finance, and Insurance. The slides of my talks are now online. I will briefly get back on three papers, about insurance of natural catastrophes, starting with Insurance against Natural Catastrophes: Balancing Actuarial Fairness and Social Solidarity, written with Molly James and Laurence Barry, and Predicting Drought and Subsidence Risks in France, written with Molly James and Hani Ali, and finally, I will get back on a more recent paper, Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach written with Menna Hassan and Nourhan Sakr.

Collaborative Insurance Sustainability and Network Structure

A second version of Collaborative Insurance Sustainability and Network Structure is now available on ArXiv,

The peer-to-peer (P2P) economy has been growing with the advent of the Internet, with well known brands such as Uber or Airbnb being examples thereof. In the insurance sector the approach is still in its infancy, but some companies have started to explore P2P-based collaborative insurance products (eg. Lemonade in the U.S. or Inspeer in France). The actuarial literature only recently started to consider those risk sharing mechanisms, as in Denuit and Robert (2021) or Feng et al. (2021). In this paper, describe and analyse such a P2P product, with some reciprocal risk sharing contracts. Here, we consider the case where policyholders still have an insurance contract, but the first self-insurance layer, below the deductible, can be shared with friends. We study the impact of the shape of the network (through the distribution of degrees) on the risk reduction. We consider also some optimal setting of the reciprocal commitments, and discuss the introduction of contracts with friends of friends to mitigate some possible drawbacks of having people without enough connections to exchange risks.

Les biais, les discriminations et l’équité en assurance

Sur Variances, un court article pour présenter le rapport remis au début de l’été à l’Institut Louis Bachelier, Assurance : Discrimination, biais et équité.

Les données massives et les performances obtenues par les algorithmes d’apprentissage automatique ont chamboulé l’assurance et l’actuariat. Les questions soulevées par ces nouveaux outils dans d’autres contextes (que ce soit la justice prédictive (ou justice “actuarielle” comme l’appelle Harcourt (2008)) ou les débats sur les fake news, en passant par les véhicules autonomes et la médecine prédictive) poussent les actuaires au doute, et à la méfiance. Kranzberg (1986) affirmait que “technology is neither good nor bad; nor is it neutral”, mettant en avant que, même sans mauvaises intentions, les algorithmes d’apprentissage pouvaient être injustes. Et corriger ces possibles injustices n’est pas simple. Pour Nielsen (2020), “technology does not necessarily self-regulate, via either market or social pressures” (la main invisible des marchés ou de la pression sociale ne suffira peut être pas). C’est dans ce contexte que nous allons revenir ici sur les problématiques de biais, de discrimination et d’équité, des modèles prédictifs utilisés en assurance. Ces changements, tant sur les données que sur les modèles, que l’on observe depuis une petite dizaine d’années, avaient déjà questionné l’existence même de l’assurance (à suivre).

Exposé sur l’équité en assurance, pour la chaire DIALog et CNP Assurances

Vendredi matin (juste avant mon cours, le premier de la session), je donnerai un exposé sur le thème “Assurance, biais, discrimination et équité“, en visio (web-coffee conference), pour la rentrée de la chaire DIALog (digital insurance and long term risk). Les slides sont en ligne. L’exposé sera largement inspiré du rapport Assurance, biais, discrimination et équité (Insurance, biaises, discrimination and fairness en anglais – la présentation sera en anglais).


Montréal AI Symposium 2022

In about ten days (Saturday afternoon), I will be presenting a poster on fairness, discrimination and insurance at the Montréal AI Symposium, based on our joint paper The Fairness of Machine Learning in Insurance: New Rags for an Old Man?, written with Laurence Barry. Since the paper was quite literary, I used material from the document Insurance: Discrimination, Biases & Fairness to get more a visual poster. Additional information will come while discussing…

What is the future of predictive probabilities in insurance?

This post was written with Laurence Barry and Ewen Gallic, in French, in November 2019 (see hal-02350006)

Insurance policies are classic examples of random contracts. This forces insurers to regularly quantify this uncertainty, to calculate probabilities in order to propose “fair” premiums for the commitments they are going to make. Isn’t it time to question this practice, at a time when artificial intelligence is exploding, offering predictive algorithms of a precision never seen before? At a time when big data / big brother could mean the disappearance of uncertainty itself?
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On consequences of Goodhart’s law

This post was initially written in French, in the Winter 2021.

As Marilyn Strathern stated, Goodhart’s Law says that “when a measure becomes a goal, it ceases to be a good measure.” There are many economic applications, but this law also helps to understand the dangers of algorithmic decisions, or to explain the difficulty of using the data available since the beginning of the SARS-CoV-2 COVID-19 pandemic.

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The scientific approach in times of crisis

This post was initially written in French, and published in April 2020.

In a conference given on February 13, 2020[i], entitled Against the Method, Didier Raoult stated “I have never done randomized trials […] to do that on infectious diseases, it makes no sense“. This view was repeated in a more detailed article, where Didier Raoult defended (what he called) “the morality [and] the humanism” of the Hippocratic oath against “the method” (and “mathematics”). As he reminds us, doing control groups is “telling the patient that we are going to give him at random either the drug we know works or the drug we do not know works” (Raoult (2020a, 2020b)). While this method of randomized experiments is now hailed in all disciplines – as the Nobel Prize in Economics awarded in 2019 to Esther Duflo, Michael Kremer and Abhijit Banerjee reminds us – how can a researcher take such a position today?
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