Dependence Modeling in Insurance and Finance: Estimation, Ratemaking and Reserving

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This program focuses on dependence modelling between highly non-continuous variables encountered in Insurance and credit risk, such as binary, count or positive variables (with potentially censoring, or point mass at zero), or bounded variables. Such data can be available either crosssectionally, or longitudinally, and are typically observed with high--dimensional covariates. For instance, in non-life insurance, one typically observes, for each individual, a claim count and a positive variable (total claim severity); in credit risk, one observes a binary indicator (the default indicator), as well as the loss--given-default (LGD) in case of default, which is bounded by 0 and 1, with possibly probability masses at bounds. In both applications, the dependence between the response variables is essential for risk management but their modeling is challenging due to the non-continuous feature. Despite the similarities of the data features, the two communities remain somehow disconnected, and face similar challenges. In particular, although recently both domains have embraced machine learning (ML) techniques, many of them have also their own downsides. My long-term objective is to i) bring the two fields closer, through the introduction of new methodologies useful in both fields, as well as appropriate technology transfer, and ii) bridge the link between traditional statistical models and machine learning (ML) techniques for dependence modelling. More precisely, my detailed objectives are as follows: -Extend the applicability of random effect models, which is a powerful family of (statistical) models for dependence modeling in insurance and credit risk, to new settings and new applications.  -Reconcile ML methods with the characteristics of financial data, and mitigate the shortcomings of existing ML methods currently proposed in the actuarial and credit risk literature. In particular I will consider ML methods that are suitable for multivariate and/or longitudinal data, so that dependence can be effectively taken into account. -Confront traditional approaches and ML methods, both empirically, and theoretically. In particular I will try to answer a long-term debate on whether and how dependence should be accounted for between credit default and the associated loss-given default, and look for new, economically-relevant model selection criteria that account for potential dependence. The expected impacts of this program is i) through training of qualified graduate students and collaboration, bridging the gap between academia and industry, ; ii) through a unified research program, bringing closer the actuarial and credit risk domains, both in industry and academia. iii) through careful analysis and comparison of both traditional and ML methods, contributing to a better understanding of the true power of ML methods for retail financial/insurance.