Publications
Insurance pricing with hierarchically structured data: An illustration with a workers' compensation insurance portfolio KU Leuven
Actuaries use predictive modeling techniques to assess the loss cost on a contract as a function of observable risk characteristics. State-of-the-art statistical and machine learning methods are not well equipped to handle hierarchically structured risk factors with a large number of levels. In this paper, we demonstrate the construction of a data-driven insurance pricing model when hierarchically structured risk factors, contract-specific as ...
Empirical Risk Assessment of Maintenance Costs under Full-service Contracts KU Leuven
We provide a data-driven framework to conduct a risk assessment, including data pre-processing, exploration, and statistical modeling, on a portfolio of full-service maintenance contracts. These contracts cover all maintenance-related costs for a fixed, upfront fee during a predetermined horizon. Charging each contract a price proportional to its risk prevents adverse selection by incentivizing low risk (i.e., maintenance-light) profiles to not ...
Copula-based inference for bivariate survival data with left truncation and dependent censoring KU Leuven
When pricing life annuity or insurance products issued to multiple lives, actuaries require a model for the survival of coupled lifetimes. For reasons of simplicity these multiple life premiums are often calculated under the assumption of independent lifetimes. In some circumstances this assumption is not realistic, and a number of correction methods based on bivariate survival data have been proposed in the actuarial literature. However, when ...
When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates KU Leuven
Technological advancements allow to develop high-performance black box predictive models. However, strictly regulated industries (like banking and insurance) ask for transparent decision-making algorithms. We therefore present a procedure to develop a Model-Agnostic Interpretable Data-driven suRRogate (maidrr) suited for structured tabular data. Knowledge is extracted from a black box via partial dependence effects. These are used to perform ...
Social network analytics for supervised fraud detection in insurance KU Leuven
Insurance fraud occurs when policyholders file claims that are exaggerated or based on intentional damages. This contribution develops a fraud detection strategy by extracting insightful information from the social network of a claim. First, we construct a network by linking claims with all their involved parties, including the policyholders, brokers, experts, and garages. Next, we establish fraud as a social phenomenon in the network and use ...
A hierarchical reserving model for reported non-life insurance claims KU Leuven
Traditional non-life reserving models largely neglect the vast amount of information collected over the lifetime of a claim. This information includes covariates describing the policy, claim cause as well as the detailed history collected during a claim’s development over time. We present the hierarchical reserving model as a modular framework for integrating a claim’s history and claim-specific covariates into the development process. ...