Using Data Analytics & Machine Learning to Design Business Interruption Insurance Products for Rail Freight Operators

• John F. Cardona ^[1] ; Juliana Castaneda ^[1] ; Leandro do C. Martins ^[1] ; Mariem Gandouz ^[1] ; Ángel A. Juan ^[1] ; Guillermo Franco
  1. [1] Universitat Oberta de Catalunya

     Universitat Oberta de Catalunya

     Barcelona, España

     
• Localización: R-evolucionando el transporte [Recurso electrónico]: XIV Congreso de Ingeniería del Transporte. Universidad de Burgos 6, 7 y 8 de julio 2021 / coord. por Hernán Gonzalo Orden, Marta Rojo Arce, 2021, ISBN 978-84-18465-12-3, págs. 487-504
• Idioma: inglés
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• Resumen

  • This paper discusses a case study in which publicly available data of a rail freight transportation firm has been gathered, cleansed, and analyzed in order to: (i) describe the data using statistical indicators and graphs; (ii) identify patterns regarding several Key.

    Performance Indicators; (iii) obtain forecasts on the future evolution of these indicators; and (iv) use the identified patterns and the generated forecasts to propose customized insuranceproducts that reflect the current and future freight transportation activity. The paper illustrates the different methodological steps required during the extraction and cleansing ofthe data --which required the development of Python scripts--, the use of time series analysisfor obtaining reliable forecasts, and the use of machine learning models for designingcustomized insurance coverage from the identified patterns and predicted values.