Tag Archives: insurance

Artificial Intelligence and Personalization of Insurance: Failure or Delayed Ignition?

Our joint paper, Artificial Intelligence and Personalization of Insurance: Failure or Delayed Ignition?, with Xavier Vamparys, has been published in Big Data & Society.

In insurance, there is still a significant gap between the anticipated disruption, due to big data and machine learning algorithms, and the actual implementation of behaviour-based personalization, as described by Meyers (2018). Here, we identify eight key factors that serve as fundamental obstacles to the radical transformation of insurance guarantees, aiming to closely align them with the risk profile of each policyholder. These obstacles include the collective nature of insurance, the entrenched beliefs of some insurance companies, challenges related to data collection and use for personalized pricing, limited interest from insurers in adopting new models as well as policyholders’ reluctance towards embracing connected devices. Additionally, the hurdles of explainability, insurer inertia and ethical or societal considerations further complicate the path toward achieving highly individualized insurance pricing.

The Insurance Market in the Era of Digital Transitions

A few months ago, we spent time with Raphaël Suire to write a short article on the insurance market, or more specifically, “The Insurance Market in the Era of Digital Transitions: Relationships Between Insurers, Big Tech, and Insurtechs“. The report is now available, on the webiste of the Society of Actuaries.

The digital revolution has profoundly transformed market dynamics, particularly within the insurance sector. This transformation encompasses the infrastructure and technologies that facilitate information exchange, the emergence of new business practices, a deluge of data, and the rise of innovative players capitalizing on these changes to deliver unique value propositions to customers. Traditional insurance companies face significant challenges and opportunities as they navigate competition from established Big Tech firms and agile insurtech startups. This study examines the disruptive nature of digital advancements, compelling historical players to confront the innovator’s dilemma (Christensen, 1997): whether to adapt and develop established practices or invest in new strategies to leverage digital opportunities. In doing so, they also come up against smaller, more agile start-ups. We highlight the necessity for insurance actors to rethink their roles in light of new market entrants and the evolving landscape shaped by Big Tech’s data monetization strategies. To analyze these dynamics, we propose an original framework in the form of a triangle of possibilities, which positions various market players and elucidates their strategic movements, innovations, and possible partnerships. This framework also aids in identifying competitive advantages and development trajectories, ultimately offering scenarios for the evolution of traditional insurance players in a digital and data-driven era.

The role of government versus private sector provision of insurance

A short paper The role of government versus private sector provision of insurance has just been published in the Journal of Risk and Insurance.

Insurance markets are important for managing risk and promoting economic stability, since they play a key role in mitigating financial losses from unpredictable events such as natural disasters, cyberattacks, and health crises. However, these markets often face challenges, including market failures, information asymmetries, and correlated risks that can destabilize private insurers. In response, governments frequently intervene in insurance markets, either by providing insurance directly or by acting as a reinsurer of last resort. The interaction between government and private sector provision of insurance raises interesting and important questions about the appropriate role of each player in ensuring market efficiency and protecting individuals and businesses from catastrophic risks.

Selection bias in insurance: why portfolio-specific fairness fails to extend market-wide

With Marie-Pier Côté and Olivier Côté, we recently upload a short note, selection bias in insurance: why portfolio-specific fairness fails to extend market-wide, now available on SSRN,

Fairness centres on people. In insurance, the scope of fairness should be the entire insured population, not solely an insurer’s clients. However, each insurance company’s portfolio represents a possibly skewed subsample. Models fit to these selection-biased data do not generalise well for the broader population of insureds. Two biases stem from portfolio composition: representation bias, when large prediction errors are made on individuals from subpopulations infrequently observed, and selection bias, when underwriting and marketing skew the portfolio away from the insured population. We examine how portfolio composition affects fair premium methodologies for mitigating direct and indirect discrimination on a protected attribute. We illustrate how unfairness mitigation based on a selection-biased portfolio does not yield a fair market from the perspective of insureds. Relying on causal inference and a portfolio composition indicator, we describe the selection mechanism and determine conditions under which each bias affects various fairness-adjusted premiums. We propose a method to recover the population-wide fairness-adjusted premiums from selection-biased data, by using a (third-party provided) unbiased estimate of the prohibited attribute distribution. We show that this approach effectively mitigates selection bias but leads to overall premiums that are not balanced. In a limiting case, we show that portfolio-specific fairness-aware premiums can lead to a market-wide unawareness strategy: portfolio composition opens the back door to proxy discrimination.

(to be continued…)

Talk at the Financial Conduct Authority, UK

This morning (Montréal time), I will give a talk for the Financial Conduct Authority in London, in the UK, on “Demystify fairness and discrimination in insurance, and avoid some pitfalls“.

What’s unique about insurance is that even statistical discrimination, which by definition is devoid of malicious intent, poses significant challenges. Because, on the one hand, policymakers would like insurers to treat their policyholders equally, without discrimination based on race, gender, age or other characteristics, even if it could make (statistical) sense to (indirectly) discriminate. On the other hand, at the core of actuaries’ activities lies discrimination, between risky and non-risky policyholders. And this risk is often statistically correlated with sensitive characteristics that regulation would like to prohibit insurers from taking into account. The analysis of possible discrimination in decision rules, whether human or algorithmic, is an old subject. Most of the concepts date back at least to the 50s, but recent developments in artificial intelligence have brought these issues back into the spotlight. Massive data facilitate statistical or proxy discrimination, and black-box algorithms do not facilitate understanding. Not to mention the various regulations that make it difficult to collect sensitive information, and ultimately test whether decisions can be discriminated against, especially indirectly.

Insurance, Biases, Discrimination and Fairness

Insurance, Biases, Discrimination and Fairness was published a few weeks ago. I still plan to spend some time this summer on the R package, including data and some functions…

This book offers an introduction to the technical foundations of discrimination and equity issues in insurance models, catering to undergraduates, postgraduates, and practitioners. It is a self-contained resource, accessible to those with a basic understanding of probability and statistics. Designed as both a reference guide and a means to develop fairer models, the book acknowledges the complexity and ambiguity surrounding the question of discrimination in insurance. In insurance, proposing differentiated premiums that accurately reflect policyholders’ true risk—termed “actuarial fairness” or “legitimate discrimination”—is economically and ethically motivated. However, such segmentation can appear discriminatory from a legal perspective. By intertwining real-life examples with academic models, the book incorporates diverse perspectives from philosophy, social sciences, economics, mathematics, and computer science. Although discrimination has long been a subject of inquiry in economics and philosophy, it has gained renewed prominence in the context of “big data,” with an abundance of proxy variables capturing sensitive attributes, and “artificial intelligence” or specifically “machine learning” techniques, which often involve less interpretable black box algorithms.

The book distinguishes between models and data to enhance our comprehension of why a model may appear unfair. It reminds us that while a model may not be inherently good or bad, it is never neutral and often represents a formalization of a world seen through potentially biased data. Furthermore, the book equips actuaries with technical tools to quantify and mitigate potential discrimination, featuring dedicated chapters that investigate into these methods.

Talk in Stockholm, Sweden, at the Insurance Data Science Conference

This week, I will attend the Insurance Data Science conference in Sweeden. It has been a while… I was a keynote speaker at the one in London, ten years ago (to give a talk I still have feedbacks about – Getting into Bayesian Wizardry… (with the eyes of a muggle actuary) – by that time, the conference was “R in Insurance”), and then, we organized the one in Paris, back in 2017. Then we had the online events, but it was… different.

This time, I will get back to our recent paper A Sequentially Fair Mechanism for Multiple Sensitive Attributes, with François Hu and Philipp Ratz, and the equipy package, wrote with Agathe Fernandes-Machado and Suzie Grondin. The slides are available online.

Trip in (Northern) Europe

The next two weeks, in will be in (Northern) Europe, with a first stop in Brussels (to visit colleagues), then in Leuven (I will give a talk on Monday at KU Leuven), then in København (I will give a talk on Friday at Københavns Universitet), and finally in Stockholm (at Stockholm University, for the Insurance Data Science conference).

In the Fall, I will be in Europe, with Lisbon (European Actuarial Journal conference), in France (Cerisy Colloques) and in Warsaw in Poland. In Poland, I will give a two day cours on Insurance, Biases, Discrimination and Fairness

More to come soon…

TD General Insurance Pricing Seminar

Tomorrow, I will give a talk at TD General Insurance Pricing Seminar, on fairness and ethics in insurance. Slides are now online.

After a very general (and long) introduction, to motivate our recent work on discrimination, I will try to explain how to quantify possible discrimination (with respect to a binary sensitive attribute), using Wasserstein distance, and optimal transport

and the use of Wasserstein Barycenter to mitigate discrimination

I will also mention our worshop in May, at Laval University,