Titel Deelnemers
"Sparse regression with Multi-type Regularized Feature modeling" "Sander Devriendt, Katrien Antonio, Tom Reynkens, Roel Verbelen"
"Modeling the number of hidden events subject to observation delay" "Jonas Crèvecoeur, Katrien Antonio, Roel Verbelen" "This paper considers the problem of predicting the number of events that have occurred in the past, but which are not yet observed due to a delay. Such delayed events are relevant in predicting the future cost of warranties, pricing maintenance contracts, determining the number of unreported claims in insurance and in modeling the outbreak of diseases. Disregarding these unobserved events results in a systematic underestimation of the event occurrence process. Our approach puts emphasis on modeling the time between the occurrence and observation of the event, the so-called observation delay. We propose a granular model for the heterogeneity in this observation delay based on the occurrence day of the event and on calendar day effects in the observation process, such as weekday and holiday effects. We illustrate this approach on a European general liability insurance data set where the occurrence of an accident is reported to the insurer with delay."
"Projecting delay and compression of mortality" "Anastasios Bardoutsos, J de Beer, F Janssen" "© 2018, The Author(s). Background: Although mortality delay (the shift of the age-at-death distribution to older ages) and mortality compression (less variability in the age at death) are the key dynamics that drove past mortality trends, they have seldom been included in mortality projections. Objective: We compare the projections of a new parametric mortality model that captures delay and compression of mortality (CoDe) with projections based on the well-known Lee-Carter (LC) model. Data and methods: We compare the two models’ properties and in-sample and out-of-sample performance using data from 1960 to 2014 for French, Japanese, and American women and men. Results: The CoDe model has less parameters to describe the shape of the age pattern, but more parameters to describe the changes in the age pattern, provides extrapolation to higher ages, allows to estimate the modal age at death, does not assume the exponential decline of rates across all ages, decomposes the delay and compression effect, and can serve as a diagnostic tool. While the LC model provides a better fit at younger ages, the CoDe model provides a better fit at older ages. The LC model consistently projects a slowdown of mortality delay and thus of the increase in life expectancy at birth, whereas the CoDe model can project a continuation of delay and thus a steady increase in life expectancy. Conclusion: Projecting mortality by including mortality delay and compression can result in better forecast performance than using the LC model, especially when the modal age at death increases linearly."
"A data driven binning strategy for the construction of insurance tariff classes" "Roel Henckaerts, Katrien Antonio, M Clijsters, Roel Verbelen" "© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. We present a fully data driven strategy to incorporate continuous risk factors and geographical information in an insurance tariff. A framework is developed that aligns flexibility with the practical requirements of an insurance company, the policyholder and the regulator. Our strategy is illustrated with an example from property and casualty (P&C) insurance, namely a motor insurance case study. We start by fitting generalized additive models (GAMs) to the number of reported claims and their corresponding severity. These models allow for flexible statistical modeling in the presence of different types of risk factors: categorical, continuous, and spatial risk factors. The goal is to bin the continuous and spatial risk factors such that categorical risk factors result which captures the effect of the covariate on the response in an accurate way, while being easy to use in a generalized linear model (GLM). This is in line with the requirement of an insurance company to construct a practical and interpretable tariff that can be explained easily to stakeholders. We propose to bin the spatial risk factor using Fisher’s natural breaks algorithm and the continuous risk factors using evolutionary trees. GLMs are fitted to the claims data with the resulting categorical risk factors. We find that the resulting GLMs approximate the original GAMs closely, and lead to a very similar premium structure."
"A data driven binning strategy for the construction of insurance tariff classes" "Roel Henckaerts, Katrien Antonio, Maxime Clijsters, Roel Verbelen" "© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. We present a fully data driven strategy to incorporate continuous risk factors and geographical information in an insurance tariff. A framework is developed that aligns flexibility with the practical requirements of an insurance company, the policyholder and the regulator. Our strategy is illustrated with an example from property and casualty (P&C) insurance, namely a motor insurance case study. We start by fitting generalized additive models (GAMs) to the number of reported claims and their corresponding severity. These models allow for flexible statistical modeling in the presence of different types of risk factors: categorical, continuous, and spatial risk factors. The goal is to bin the continuous and spatial risk factors such that categorical risk factors result which captures the effect of the covariate on the response in an accurate way, while being easy to use in a generalized linear model (GLM). This is in line with the requirement of an insurance company to construct a practical and interpretable tariff that can be explained easily to stakeholders. We propose to bin the spatial risk factor using Fisher’s natural breaks algorithm and the continuous risk factors using evolutionary trees. GLMs are fitted to the claims data with the resulting categorical risk factors. We find that the resulting GLMs approximate the original GAMs closely, and lead to a very similar premium structure."
"Unraveling the predictive power of telematics data in car insurance pricing" "Roel Verbelen, Katrien Antonio, Gerda Claeskens" "A data set from a Belgian telematics product aimed at young drivers is used to identify how car insurance premiums can be designed based on the telematics data collected by a black box installed in the vehicle. In traditional pricing models for car insurance, the premium depends on self-reported rating variables (e.g. age, postal code) which capture characteristics of the policy(holder) and the insured vehicle and are often only indirectly related to the accident risk. Using telematics technology enables tailor-made car insurance pricing based on the drivingbehavior of the policyholder. We develop a statistical modeling approach using generalized additive models and compositional predictors to quantify and interpret the effect of telematics variables on the expected claim frequency. We find that such variables increase the predictive power and render the use of gender as a rating variable redundant."
"Modelling censored losses using splicing: a global fit strategy with mixed Erlang and extreme value distributions" "Tom Reynkens, Roel Verbelen, Jan Beirlant, Katrien Antonio" "© 2017 Elsevier B.V. In risk analysis, a global fit that appropriately captures the body and the tail of the distribution of losses is essential. Modelling the whole range of the losses using a standard distribution is usually very hard and often impossible due to the specific characteristics of the body and the tail of the loss distribution. A possible solution is to combine two distributions in a splicing model: a light-tailed distribution for the body which covers light and moderate losses, and a heavy-tailed distribution for the tail to capture large losses. We propose a splicing model with a mixed Erlang (ME) distribution for the body and a Pareto distribution for the tail. This combines the flexibility of the ME distribution with the ability of the Pareto distribution to model extreme values. We extend our splicing approach for censored and/or truncated data. Relevant examples of such data can be found in financial risk analysis. We illustrate the flexibility of this splicing model using practical examples from risk measurement."
"Maximum human lifespan may increase to 125 years" "Joop de Beer, Anastasios Bardoutsos, Fanny Janssen"
"A Bayesian joint model for population and portfolio-specific mortality" "F Van Berkum, Katrien Antonio, M Vellekoop"
"La tarification par genre en assurance: corrélation ou causalité?" "A Charpentier, Katrien Antonio"