This Thursday, Julien Trufin will be giving a talk at the CANSSI SSC Seminar, live from Montréal
Machine learning techniques provide actuaries with predictors exhibiting high correlation with claim frequencies and severities. However, these predictors generally fail to achieve financial equilibrium and thus do not qualify as pure premiums. Autocalibration effectively addresses this issue since it ensures that every group of policyholders paying the same premium is on average self-financing. This talk proposes to look at recent results concerning autocalibration. In particular, we present a new characterization of autocalibration which enables us to identify whether a predictor is autocalibrated or not, we study a method (called balance correction) for obtaining an autocalibrated predictor from any regression model, we highlight the effect of balance correction on resulting pure premiums, and finally we go through some performance criteria that are particularly relevant for autocalibrated predictors.
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.
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.
La semaine dernière, j’assistais à une série d’exposés sur les algorithmes, l’IA, les inégalités, les injustices, etc, et j’avais l’impression que beaucoup de monde se trompait de combat, en mettant beaucoup de choses “sur le dos des algorithmes”… Je vais en profiter pour ressortir un article que j’avais écrit voilà quelques années… “L’intelligence artificielle dilue-t-elle la responsabilité ?”
On essaie de nous faire croire que l’intelligence artificielle est une révolution. Et s’il n’en était rien ? Ne peut-on pas voir tout simplement la logique d’un processus qui remonte au moins aux cinquante dernières années ? La bureaucratie nous a poussés à mettre en place dans tous les domaines de la vie quotidienne des procédures simples, permettant à tout le monde de se dégager de toute responsabilité, de ne plus avoir à faire preuve d’intelligence. Les algorithmes font peur ; on se demande où se trouve l’« humain » dans ces procédures décisionnelles… Et s’il avait déjà disparu depuis bien longtemps ?
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.
"sendo l'intento mio scrivere cosa utile a chi la intende…"
