Creación de un paquete de R para gestionar bases de datos de preguntas de test compatibles con Moodle

Aplicación que permite gestionar una base de datos de preguntas de test e importarlas en moodle

Desarrollo de aplicaciones informáticas para agilizar la creación y el uso de cuestionaros de tipo test a través del campus virtual

El presente proyecto es continuación de otro desarrollado en la convocatoria del año pasado. Con este último se pretendía desarrollar una aplicación escrita en lenguaje R para crear y gestionar de forma cómoda bases de datos de preguntas de test destinadas a cuestionarios en Moodle

See5 algorithm versus discriminant analysis. An application to the prediction of insolvency in Spanish non-life insurance companies

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

Sistemas de inducción de reglas y árboles de decisión aplicados a la predicción de insolvencias en empresas aseguradoras

Tradicionalmente, para abordar el problema de la detección precoz de la insolvencia empresarial, se han venido utilizando métodos estadísticos que emplean ratios financieros como variables explicativas. Sin embargo, aunque la eficacia de dichos métodos ha sido sobradamente probada, presentan algunos problemas que dificultan su aplicación en el ámbito empresarial, ya que, generalmente, se trata de modelos basados en una serie de hipótesis sobre las variables explicativas que en muchos casos no se cumplen y, además, dada su complejidad, puede resultar difícil extraer conclusiones de sus resultados para un usuario poco familiarizado con la técnica. El presente trabajo describe una investigación de carácter empírico consistente en la aplicación al sector asegurador del algoritmo de inducción de reglas y árboles de decisión See5, a partir de un conjunto de ratios financieros de una muestra de empresas españolas de seguros no-vida, con el objeto de comprobar su utilidad para la predicción de insolvencias en este sector. También se comparan los resultados alcanzados con los que se obtienen aplicando la metodología Rough Set. Estas técnicas, procedentes del campo de la Inteligencia Artificial, no presentan los problemas mencionados anteriormente

Rough Sets in insurance sector

Rough Set theory methodology belongs to the domain of Artificial Intelligence (AI) and has demonstrated a very high performance in financial issues, especially in classifying problems. Yet, there is little AI research devoted to the insurance industry, although it plays a growing and crucial role in modern economies. The present chapter shows three relevant rough sets researches in insurance sector concluding that this method is an effective tool for supporting managerial decision making in general, and for insurance sector in particular.Depto. de Economía Financiera y Actuarial y EstadísticaFac. de Ciencias Económicas y EmpresarialesTRUEpu

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

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