Machine learning and statistical techniques. An application to the prediction of insolvency in spanish non-life insurance companies

  1. Díaz Martínez, Zuleyka
  2. Segovia Vargas, María Jesús
  3. Fernández Menéndez, José
  4. Pozo García, Eva María del
Revista:
The International Journal of Digital Accounting Research

ISSN: 1577-8517

Año de publicación: 2005

Volumen: 5

Número: 9

Páginas: 1-45

Tipo: Artículo

DOI: 10.4192/1577-8517-V5_1 DIALNET GOOGLE SCHOLAR lock_openArias Montano editor

Otras publicaciones en: The International Journal of Digital Accounting Research

Objetivos de desarrollo sostenible

Resumen

Prediction of insurance companies insolvency has arisen as an important problem in the field of financial research. Most methods applied in the past to tackle this issue are traditional statistical techniques which use financial ratios as explicative variables. However, these variables often do not satisfy statistical assumptions, which complicates the application of the mentioned methods. In this paper, a comparative study of the performance of two non-parametric machine learning techniques (See5 and Rough Set) is carried out. We have applied the two methods to the problem of the prediction of insolvency of Spanish non-life insurance companies, upon the basis of a set of financial ratios. We also compare these methods with three classical and well-known techniques: one of them belonging to the field of Machine Learning (Multilayer Perceptron) and two statistical ones (Linear Discriminant Analysis and Logistic Regression). Results indicate a higher performance of the machine learning techniques. Furthermore, See5 and Rough Set provide easily understandable and interpretable decision models, which shows that these methods can be a useful tool to evaluate insolvency of insurance firms.