At the end of May, on Sunday May 28th, I will participate to the workshop Machine Learning and Data Mining in Insurance and Finance, at the Statistical Society of Canada Annual Meeting
Machine learning and data mining are hot research topics in insurance and finance and are heavily used in the industry of insurance and finance as well. In this workshop, the four invited speakers will introduce methods of machine learning and data mining and discuss their applications in insurance and finance. Each of the four speakers has 60 minutes for the presentation. There in a ten-minute break after a presentation. The total duration of the workshop is four and half hours.
Ce midi, je donnerai un exposé chez Beneva sur “Tarification en assurance dans un marché concurrentiel”.
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.
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?
Continue reading What is the future of predictive probabilities in insurance? →
This post was initially published in French in September 2021.
The essential role of an actuary in charge of pricing is the segmentation of the portfolio (or “insurance classification” in English), corresponding to a discrimination activity (mathematically speaking) in the sense that the actuary will look for the most “discriminating” variables, to explain another one (in relation with the loss experience). But in the legal sense, discrimination is forbidden by law, which places the actuary in an often delicate and complex position.
Continue reading Insurance and discrimination, what role for actuaries? →
with Vincent Grari, Sylvain Lamprier and Marcin Detyniecki we recently uploaded a paper a fair pricing model via adversarial learning on ArXiv
At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher premium than non-risky or less-risky ones. Actuaries, therefore, use econometric or machine learning techniques to classify, but the distinction between a fair actuarial classification and “discrimination” is subtle. For this reason, there is a growing interest about fairness and discrimination in the actuarial community Lindholm, Richman, Tsanakas, and Wuthrich (2022). Presumably, non-sensitive characteristics can serve as substitutes or proxies for protected attributes. For example, the color and model of a car, combined with the driver’s occupation, may lead to an undesirable gender bias in the prediction of car insurance prices. Surprisingly, we will show that debiasing the predictor alone may be insufficient to maintain adequate accuracy (1). Indeed, the traditional pricing model is currently built in a two-stage structure that considers many potentially biased components such as car or geographic risks. We will show that this traditional structure has significant limitations in achieving fairness. For this reason, we have developed a novel pricing model approach. Recently some approaches have Blier-Wong, Cossette, Lamontagne, and Marceau (2021); Wuthrich and Merz (2021) shown the value of autoencoders in pricing. In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.
Ce vendredi, je donnerai un exposé au groupe de travail d’IVADO, invité par Dany Plourde. Les slides sont maintenant en ligne. En guise d’introduction, cette petite anecdote
L’exposé sera assez standard, avec une rapide introduction sur l’assurance, avant de revenir sur trois notions clés: la discrimination, les biais et les mesures d’équité
Comme c’est la première fois que je présente au Québec, je vais revenir sur quelques statistiques de marché
et une comparaison d’états aux États-Unis, et de provinces au Canada, pour caractériser des possibles discriminations
Sur les biais, je reviendrais deux de mes préférées, le paradoxe de Simpson et l’inférence écologique
et la loi de Goodhard, et le biais de rétroaction
On finira avec quelques définitions de l’équité
Tomorrow, and for the next three days, we organize the Insurance Data Science Conference, with Andreas, Markus and Ioannis in London, and Silvana in Toronto. Talks will take place in the afternoon in Europe, and in the morning in Canada…
A few months ago, I published a short article, Big data, the tech giants, and insurance, in the Annales de Mines. The original article was in French, but the Editors shared an English version,
Technology and insurance companies seem like polar opposites in every possible way. The tech giants, agile and fast-acting, are obsessed with the future, whereas insurers, conservative and reflexive, are fascinated with the data that the tech giants collect. However these two sectors are now eyeing each other and have started forming partnerships as they come to understand that, in both cases, their core business is data.
to be continued…
Registrations and call for abstracts, for the 3rd Insurance Data Science Conference, organised on-line 16 – 18 June 2021 (PM in Europe, AM in America), are now open. See https://insurancedatascience.org/ for more details…
Since 2010, the tech giants—particularly the “3As” (Amazon, Apple, and Alphabet [Google’s parent company])—have begun to expand their horizons in pursuit of new business opportunities. From retail to the automotive sector, these companies have learned to use the colossal competitive advantages built on data analysis, user relationships, and the skills of innovative computer engineers to bring about a profound transformation of certain markets. It was only a matter of time before they began to turn their thoughts to insurance and take their first steps in that world. Here, we will be looking at the specific cases of health, motoring, and home insurance, demonstrating how new partnerships are striving to come up with innovative solutions. We will also be exploring the consequences of these changes, which invite us to reevaluate the role of data, now once again at the core of the insurance profession. Finally, we will consider whether this innovation could in fact be seen as a return to the roots of the very concept of insurance: the pooling and distribution of risk… (to be continued on cairn-int.info)
The Insurance Data Science Conference returns in 2021 for an on-line global event. The conference will run over three half-days (afternoons in Europe & Africa / mornings in the Americas). The conference brings together academics and practitioners in areas including data science, analytics, machine learning, artificial intelligence, computational statistics and software, as applied in the insurance industry. For more information, see https://insurancedatascience.org/
This week is organized the summer school on machine learning for economists and applied social scientists. I will be giving an (online) lecture this Thursday, on Machine Learning in Actuarial Science & Insurance, with a great program,
- 10am – 10:55am : Juri Marcucci: Machine Learning in Macroeconomics
- 11am – 11:55am : Arthur Charpentier: Machine Learning in Actuarial Science & Insurance
- 12pm – 12:55pm : Arthur Spirling : Machine Learning in Embeddings Representations
- 1pm – 1:55pm : Kathy Baylis: Machine Learning in Agricultural Economics
- 2pm – 2:55pm : Stefan Wager : Machine Learning in Causal Inference
- 10am – 10:55am : Stan Matwin : Machine Learning and Data Privacy
- 11am – 11:55am : Mehmet Caner : Machine Learning in Econometrics
- 12pm – 12:55pm : Anders Bredahl Kock : Machine Learning in Model Selection
- 1pm – 1:55pm : Dario Sansone: Machine Learning in Education and Development Economics
- 2pm – 2:55pm: Patrick Baylis :Temperature and Temperament: Evidence from Twitter
My slides are now online,
Just a brief post to mention that was invited as a keynote speaker to give a talk at the Summer School on Machine Learning for Economists, in one month,
I will give a talk on machine learning and insurance. More to come, soon…