Prof. dr. K. (Katrien) Antonio
Faculty of Economics and Business
Section Quantitative Economics
Visiting address
- Roetersstraat 11
- Room number: 4.22
Postal address
- Postbus 15867
1001 NJ Amsterdam
Contact details
Social media
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Profiel
Bio
- Professor in actuarial data science at the University of Amsterdam (part time) and professor in actuariel science and insurance analytics at KU Leuven (main affiliation).
- MSc in Mathematics (KU Leuven, 2003) and PhD in statistics and actuarial science (KU Leuven, 2007).
Personal homepage:
For more information please visit https://katrienantonio.github.io.
Research interests
- Insurance data science, insurance analytics.
- Actuarial science.
- Quantitative risk modeling.
- Pricing and reserving for non-life, life and health insurance.
- Mortality modeling and forecasting.
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Publications
2022
- Campo, B., & Antonio, K. (Accepted/In press). Insurance pricing with hierarchically structured data: an illustration with a workers' compensation insurance portfolio. Scandinavian Actuarial Journal. https://arxiv.org/abs/2206.15244
- Crevecoeur, J., Robben, J., & Antonio, K. (2022). A hierarchical reserving model for reported non-life insurance claims. Insurance: Mathematics & Economics, 104, 158-184. https://doi.org/10.1016/j.insmatheco.2022.02.005 [details]
- Deprez, L., Antonio, K., & Boute, R. (2022). Empirical risk assessment of maintenance costs under full-service contracts. European Journal of Operational Research, 304(2), 476. https://doi.org/10.1016/j.ejor.2022.03.055
- Deresa, N. W., Van Keilegom, I., & Antonio, K. (2022). Copula-based inference for bivariate survival data with left truncation and dependent censoring. Insurance: Mathematics and Economics, 107, 1-21. https://doi.org/10.1016/j.insmatheco.2022.07.011 [details]
- Henckaerts, R., & Antonio, K. (2022). The added value of dynamically updating motor insurance prices with telematics collected driving behavior data. Insurance: Mathematics & Economics, 105, 79. https://doi.org/10.1016/j.insmatheco.2022.03.011
- Henckaerts, R., Antonio, K., & Côté, M-P. (2022). When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates. Expert Systems With Applications, 202(117230). https://doi.org/10.1016/j.eswa.2022.117230
- Oskarsdottir, M., Ahmed, W., Antonio, K., Baesens, B., Dendievel, R., Donas, T., & Reynkens, T. (2022). Social network analytics for supervised fraud detection in insurance. Risk Analysis, 42(8), 1872. https://doi.org/10.1111/risa.13693
Robben, J., Antonio, K., & Devriendt, S. (2022). Assessing the Impact of the COVID-19 Shock on a Stochastic Multi-Population Mortality Model. Risks, 10(2), [26]. https://doi.org/10.3390/risks10020026 [details]- Verbelen, R., Antonio, K., Claeskens, G., & Crevecoeur, J. (2022). Modeling the occurrence of events subject to a reporting delay via an EM algorithm. Statistical Science, 37(3), 394. https://doi.org/10.48550/arXiv.1909.08336
2021
- Antonio, K., Dutang, C., & Tsanakas, A. (2021). Editorial - special issue insurance data science. Annals of Actuarial Science, 15(2), 205-206. https://doi.org/10.1017/S174849952100018X
- Deprez, L., Antonio, K., & Boute, R. (2021). Pricing service maintenance contracts using predictive analytics. European Journal of Operational Research, 290(2), 530-545. https://doi.org/10.1016/j.ejor.2020.08.022
- Devriendt, S., Antonio, K., Reynkens, T., & Verbelen, R. (2021). Sparse regression with Multi-type Regularized Feature modeling. Insurance: Mathematics & Economics, 96, 248-261. https://doi.org/10.1016/j.insmatheco.2020.11.010
- Henckaerts, R., Côté, M-P., Antonio, K., & Verbelen, R. (2021). Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods. North American Actuarial Journal, 25(2), 255-285. https://doi.org/10.1080/10920277.2020.1745656
- van Berkum, F., Antonio, K., & Vellekoop, M. (2021). Quantifying longevity gaps using micro-level lifetime data. Journal of the Royal Statistical Society. Series A: Statistics in Society, 184(2), 548-570. https://doi.org/10.1111/rssa.12631 [details]
2019
- Crevecoeur, J., Antonio, K., & Verbelen, R. (2019). Modeling the number of hidden events subject to observation delay. European Journal of Operational Research, 277(3), 930-944. https://doi.org/10.1016/j.ejor.2019.02.044 [details]
2018
- Henckaerts, R., Antonio, K., Clijsters, M., & Verbelen, R. (2018). A data driven binning strategy for the construction of insurance tariff classes. Scandinavian Actuarial Journal, 2018(8), 681-705. https://doi.org/10.1080/03461238.2018.1429300 [details]
- Stripling, E., vanden Broucke, S., Antonio, K., Baesens, B., & Snoeck, M. (2018). Profit maximizing logistic model for customer churn prediction using genetic algorithms. Swarm and Evolutionary Computation, 40, 116-130. https://doi.org/10.1016/j.swevo.2017.10.010 [details]
- Verbelen, R., Antonio, K., & Claeskens, G. (2018). Unravelling the predictive power of telematics data in car insurance pricing. Journal of the Royal Statistical Society. Series C: Applied Statistics, 67(5), 1275-1304. https://doi.org/10.1111/rssc.12283 [details]
2017
- Antonio, K., Devriendt, S., de Boer, W., de Vries, R., De Waegenaere, A., Kan, H-K., Kromme, E., Ouburg, W., Schulteis, T., Slagter, E., van der Winden, M., van Iersel, C., & Vellekoop, M. (2017). Producing the Dutch and Belgian mortality projections: a stochastic multi-population standard. European Actuarial Journal, 7(2), 297-336. https://doi.org/10.1007/s13385-017-0159-x [details]
- Dhaene, J., Godecharle, E., Antonio, K., Denuit, M., & Hanbali, H. (2017). Lifelong health insurance covers with surrender values: updating mechanisms in the presence of medical inflation. ASTIN Bulletin, 47(3), 803-836. https://doi.org/10.1017/asb.2017.13 [details]
- Reynkens, T., Verbelen, R., Beirlant, J., & Antonio, K. (2017). Modelling censored losses using splicing: a global fit strategy with mixed Erlang and extreme value distributions. Insurance: Mathematics & Economics, 77, 65-77. https://doi.org/10.1016/j.insmatheco.2017.08.005 [details]
- van Berkum, F., Antonio, K., & Vellekoop, M. (2017). A Bayesian joint model for population and portfolio-specific mortality. ASTIN Bulletin, 47(3), 681-713. https://doi.org/10.1017/asb.2017.17 [details]
2016
- Verbelen, R., Antonio, K., & Claeskens, G. (2016). Multivariate mixtures of Erlangs for density estimation under censoring. Lifetime Data Analysis, 22(3), 429-455. https://doi.org/10.1007/s10985-015-9343-y [details]
- van Berkum, F., Antonio, K., & Vellekoop, M. (2016). The impact of multiple structural changes on mortality predictions. Scandinavian Actuarial Journal, 2016(7), 581-603. https://doi.org/10.1080/03461238.2014.987807 [details]
2015
- Antonio, K., Bardoutsos, A., & Ouburg, W. (2015). Bayesian Poisson log-bilinear models for mortality projections with multiple populations. European Actuarial Journal, 5(2), 245-281. https://doi.org/10.1007/s13385-015-0115-6 [details]
- Godecharle, E., & Antonio, K. (2015). Reserving by conditioning on markers of individual claims: a case study using historical simulation. North American Actuarial Journal, 19(4), 273-288. https://doi.org/10.1080/10920277.2015.1046607 [details]
- Verbelen, R., Gong, L., Antonio, K., Badescu, A., & Lin, S. (2015). Fitting mixtures of Erlangs to censored and truncated data using the EM algorithm. ASTIN Bulletin, 45(3), 729-758. https://doi.org/10.1017/asb.2015.15 [details]
2014
- Antonio, K., & Plat, R. (2014). Micro-level stochastic loss reserving for general insurance. Scandinavian Actuarial Journal, 2014(7), 649-699. https://doi.org/10.1080/03461238.2012.755938 [details]
- Antonio, K., & Zhang, Y. (2014). Linear mixed models. In E. W. Frees, R. A. Derrig, & G. Meyers (Eds.), Predictive modeling applications in actuarial science - Vol. 1: predictive modeling techniques (pp. 182-216). (International Series on Actuarial Science). New York: Cambridge University Press. [details]
- Antonio, K., & Zhang, Y. (2014). Nonlinear mixed models. In E. W. Frees, R. A. Derrig, & G. Meyers (Eds.), Predictive modeling applications in actuarial science - Vol. 1: predictive modeling techniques (pp. 398-426). (International Series on Actuarial Science). New York: Cambridge University Press. [details]
- Antonio, K., Shi, P., & van Berkum, F. (2014). Longitudinal data and experience rating. In A. Charpentier (Ed.), Computational actuarial science with R (pp. 511-542). (Chapman & Hall/CRC The R Series). Boca Raton: CRC Press. [details]
- Pigeon, M., Antonio, K., & Denuit, M. (2014). Individual loss reserving using paid-incurred data. Insurance: Mathematics & Economics, 58, 121-131. https://doi.org/10.1016/j.insmatheco.2014.06.012 [details]
2013
- Pigeon, M., Antonio, K., & Denuit, M. (2013). Individual loss reserving with the multivariate skew normal framework. ASTIN Bulletin, 43(3), 399-428. https://doi.org/10.1017/asb.2013.20 [details]
- Vercruysse, W., Dhaene, J., Denuit, M., Pitacco, E., & Antonio, K. (2013). Premium indexing in lifelong health insurance. Far East Journal of Mathematical Sciences, Special Volume(4), 365-384. http://www.pphmj.com/abstract/7855.htm [details]
2012
- Antonio, K., & Valdez, E. (2012). Statistical concepts of a priori and a posteriori risk classification in insurance. AStA-Advances in Statistical Analysis, 96(2), 187-224. https://doi.org/10.1007/s10182-011-0152-7 [details]
2010
- Antonio, K., Frees, E. W., & Valdez, E. A. (2010). A multilevel analysis of intercompany claim counts. ASTIN Bulletin, 40(1), 151-177. https://doi.org/10.2143/AST.40.1.2049223 [details]
2008
- Antonio, K., & Beirlant, J. (2008). Issues in claims reserving and credibility: a semiparametric approach with mixed models. The Journal of Risk and Insurance, 75(3), 643-676. https://doi.org/10.1111/j.1539-6975.2008.00278.x [details]
- Antonio, K., & Beirlant, J. (2008). Risk classification in nonlife insurance. In E. L. Melnick, & B. S. Everitt (Eds.), Encyclopedia of quantitative risk analysis and assessment. - Vol. 4 (pp. 1530-1535). Chichester [etc.]: Wiley. [details]
- van Calster, H., Endels, P., Antonio, K., Verheyen, K., & Hermy, M. (2008). Coppice management effects on experimentally established populations of three herbaceous layer woodland species. Biological Conservation, 141(10), 2641-2652. https://doi.org/10.1016/j.biocon.2008.08.001 [details]
2007
- Antonio, K., & Beirlant, J. (2007). Actuarial statistics with generalized linear mixed models. Insurance: Mathematics & Economics, 40(1), 58-76. https://doi.org/10.1016/j.insmatheco.2006.02.013 [details]
2006
- Antonio, K., Beirlant, J., Hoedemakers, T., & Verlaak, R. (2006). Lognormal mixed models for reported claims reserves. North American Actuarial Journal, 10(1), 30-48. https://doi.org/10.1080/10920277.2006.10596238
2005
- Antonio, K., Beirlant, J., & Hoedemakers, T. (2005). Discussion on 'A Bayesian generalized linear model for the Bornhuetter-Ferguson method of claims reserving'. North American Actuarial Journal, 9(3), 143-145.
2017
- Antonio, K., & Charpentier, A. (2017). La tarification par genre en assurance: corrélation ou causalité? Risques, 109, 107-110. [details]
2015
- Antonio, K., & Devriendt, S. (2015). Lang leven in België: een nieuwe prognose. (Leuvense Economische Standpunten; No. LES 2015/151). Leuven: KU Leuven. [details]
2013
- Antonio, K., & Plat, R. (2013). Stochastische schadereservering op microniveau. In F. Thooft (Ed.), 50 jaar ASTIN: verleden, heden en toekomst (pp. 28-31). Utrecht: Koninklijk Actuarieel Genootschap. [details]
2012
- Antonio, K. (2012). Bijlage bij prognosetafel AG2012-2062: sluiten van de periodetafel GBM/V 2005-2010. Utrecht: Actuarieel Genootschap & Actuarieel Instituut. [details]
- Antonio, K., & Plat, R. (2012). Schadereservering anders?: van driehoeken naar micro-data. Actuaris, 19(6), 32-34. [details]
- Antonio, K., van der Heijden, A. M. J. H., Meijer, R. E. V., Smit, C. T., Tornij, J. H., de Vries, R. W. J., ... van Zijp, P. P. C. (2012). Prognosetafel AG 2012-2062. Utrecht: Het Actuarieel Genootschap / Actuarieel Instituut. [details]
2010
2008
- Antonio, K. (2008). Statistical tools for non-life insurance. Aenorm, 59, 5-9. [details]
This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list. -
Ancillary activities
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KU Leuven
gewoon hoogleraar actuariële wetenschappen
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KU Leuven
Robben, J., Antonio, K., & Devriendt, S. (2022). Assessing the Impact of the COVID-19 Shock on a Stochastic Multi-Population Mortality Model. Risks, 10(2), [26].