Project TED2021-130187B-I00: ANALITICA DE DATOS EN SEGUROS: METODOS E IMPLICACIONES PARA PRODUCTOS BASADOS EN EL USO (IDA4UB)

Summary

The aim of this project is the development of methods, algorithms and protocols for the treatment of massive data specially designed for risk management. In this area there are some particular features:

1) information provided by sensors is available at very short time intervals (for example measurements like speed or acceleration may be recorded every second)

2) unstructured or external information may be combined (for example, the number and duration of trips is not homogeneous for all vehicles) and individuals may have heterogeneous exposure to risk and

3) telematics data should be merged with information on the occurrence and severity of accidents (which are much more infrequent data than telematics).

The ultimate goal is to obtain effective levels of protection.

Financiado por MICIU/AEI/10.13039/501100011033 y por la Unión Europea NextGenerationEU/PRTR

Publications (2022-2025)

1. Masello, L., Sheehan, B., Castignani, G., Guillen, M., Murphy, F. (2024) “Predictive Modeling for Driver Insurance Premium Calculation using Advanced Driver Assistance Systems and Contextual Information”. IEEE-Intelligent Transportation Systems Transactions, https://doi.org/10.1109/TITS.2024.3518572

2. Yanez, J. S., Guillén, M., Nielsen, J. P. (2024) “Weekly Dynamic Motor Insurance Ratemaking with a Telematics Signals Bonus-Malus Score.” ASTIN Bulletin: 1–28. https://doi.org/10.1017/asb.2024.30

3. Salas-Molina, F., Rodriguez Aguilar, J.A. and Guillen, M. (2023) “A multidimensional review of the cash management problem” Financial Innovation, 9(1), 67. https://doi.org/10.1186/s40854-023-00473-7

4. Masello, L., Castignani, G., Sheehan, B., Guillen, M. and Murphy, F. (2023) “Using contextual data to predict risky driving events: A novel methodology from explainable artificial intelligence” Accident Analysis & Prevention, 184, 106997. https://doi.org/10.1016/j.aap.2023.106997

5. Restrepo, N., Uribe, J. M. and Guillen, M. (2023) “Price bubbles in lithium markets around the world” Frontiers in Energy Research, 11, 1204179. https://doi.org/10.3389/fenrg.2023.1204179

6. Guillen, M., Santolino, M. and Vidal-Llana, X. (2024). “Desigualdad de la incertidumbre económica subjetiva y perspectivas económicas individuales durante la pandemia”. Revista de Métodos Cuantitativos para la Economía y la Empresa, 1-18. https://doi.org/10.46661/ rev.metodoscuant.econ.empresa.7558

7. Guillen, M., Pérez-Marín, A.M. and Nielsen, J.P. (2024). “Pricing weekly motor insurance drivers’ with behavioral and contextual telematics data”. Heliyon, Volume 10, Issue 16. https://doi.org/10.1016/j.heliyon.2024.e36501

8. Bolancé, C., Cao, R., and Guillén, M. (2023). “Conditional likelihood based inference on single index-models for motor insurance claim severity”. SORT-Statistics and Operations Research Transactions, 48(2). https://doi.org/10.57645/20.8080.02.20

9. Reig Torra, J., Guillen, M., Pérez-Marín, A. M., Rey Gámez, L. and Aguer, G. (2023). “Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models”. Risks, 11(3), 57. https://doi.org/10.3390/risks11030057

Contributions to meetings (2022-2024)

1. Yáñez, J.S., Guillén, M. (2025) “Total Annual Distance Driven versus Total Annual Driving Time in the Risk of causing a Traffic Accident”. SEIO 2025

2. Vidal-Llana, X., Morillas Jurado, F. G (2025) “Exploring Dependence Structures in Asset Pricing: Advancements in Multivariate Quantile-on-Quantile Regression”. SEIO 2025

3. Moriña, D. (2025) “Estimación de la magnitud de la incidencia de eventos de ciberseguridad no detectados en España”. SEIO 2025

4. Moriña, D. (2024) “Presentación de los trabajos realizados en el proyecto IDA4UB”. Reunión de coordinación de la red Nacional de Bioestadística, BIOSTATNET. Valencia.

5. Guillén, M. (2024) “The future of long-term saving: the research perspective”. Innovative Pathways: Pensions, Fintech, and the Evolving Landscape. Bayes Business School, co-organized with Universities Superannuation Scheme Limited (USS). London

6. Guillén, M. (2024) “Telematics Insights on Time vs. Distance in Motor Insurance Pricing”. Joint Conference of the Sections of the International Actuarial Association – JOCO 2024. Brussels, Belgium.

7. Guillén, M. (2024) “Telematics driving scores, real-time pricing and exposure to risk”. St. Gallen Actuarial Seminar, Switzerland

8. Guillén, M., Pérez-Marín, A.M. (2023) “Motor insurance with telematics driving data”. ARC Actuarial Research Conference, Drake University, Des Moines, USA

9. Guillén, M. (2023) “Pricing motor insurance with telematics data”. Chaire PARI. Paris, France

10. Bagkavos, D., Nielsen, J.P. and Guillen, M. (2023) “Nonparametric conditional survival function estimation with plug-in bandwidth and robust model selection”. 35th Panhellenic and 1st International Statistics Conference Greek Statistical Institute. Athens, Greece

11. Guillén, M., Pérez-Marin, A.M. and Vidal-Llana, J.J (2023). “Non-crossing neural network quantile regression estimation for driving data with telematics”. ARIA Annual Meeting. Washington DC

12. Vidal-Llana, J.J. and Guillén, M. (2023) “Non-Crossing Neural Network Quantile Regression Estimation for Driving Data with Telematics”. European Actuarial Day

13. Vidal-Llana, J.J. and Guillén, M. (2023) “Non-crossing neural network regression estimation for driving data with Telematics”. Insurance Data Science, City, University of London

14. Vidal-Llana, X., Salort, C. Coia, V. and Guillen M. (2023) “Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation regression with non-crossing conditions”. IME, Heriot-Watt University, Edinburg

15. Fernández-Fontelo. A., Guillen, M. and Moriña, D. (2023) “Measuring the impact of Covid-pandemic on health insurance associated services demand”. SEIO 2023

16. Vidal-Llana, X. and Guillen, M. (2023) “Non-crossing neural network quantile regression estimation for driving data with telematics”. SEIO 2023

17. Vidal-Llana, X. and Guillen, M. (2024) “Quantile regression and portfolio refinement: addressing extreme behaviors in risk management”. European Actuarial Journal Conference 2024

PhD theses defended

1.Vidal Llana, X. (2023) “Essays on Machine Learning for Risk Analysis in Finance, Insurance and Energy” PhD in Business. Dir.: Montserrat Guillénand Jorge Mario Uribe