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oai:docta.ucm.es:20.500.14352/565642025-07-02T13:50:12Zcom_20.500.14352_14col_20.500.14352_17

00925njm 22002777a 4500 dc Díaz Martínez, Zuleyka author Fernández Menéndez, José author Segovia Vargas, María Jesús author 2004 Prediction of insurance companies insolvency has arised as an important problem in the field of financial research, due to the necessity of protecting the general public whilst minimizing the costs associated to this problem. Most methods applied in the past to tackle this question are traditional statistical techniques which use financial ratios as explicative variables. However, these variables do not usually satisfy statistical assumptions, what complicates the application of the mentioned methods. In this paper, a comparative study of the performance of a well-known parametric statistical technique (Linear Discriminant Analysis) and a non-parametric machine learning technique (See5) 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. Results indicate a higher performance of the machine learning technique, what shows that this method can be a useful tool to evaluate insolvency of insurance firms. 2255-5471 https://hdl.handle.net/20.500.14352/56564 https://economicasyempresariales.ucm.es/working-papers-ccee See5 algorithm versus discriminant analysis. An application to the prediction of insolvency in Spanish non-life insurance companies