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.El pronóstico sobre la insolvencia de las compañías de seguro ha surgido como un problema importante en el ámbito de investigación financiera. La mayoría de los métodos aplicados en el pasado para abordar este problema, son técnicas estadísticas tradicionales que usan los ratios financieros como variables explicativas. Aunque, estas variables a menudo no satisfacen las suposiciones estadísticas, lo que complica la aplicación de dichos métodos. En este artículo, se lleva a cabo un estudio comparativo sobre la actuación de dos técnicas de aprendizaje automático no paramétrico (See5 y Rough Set). Hemos aplicado ambos métodos al problema del pronóstico sobre la insolvencia de compañías españolas de seguros no de vida, sobre la base de un conjunto de ratios financieros. Además, hemos comparado estos métodos con tres técnicas clásicas y muy conocidas: una de ellas perteneciente al área del Aprendizaje Automático (Perceptrón Multicapa), y dos estadísticos (Análisis Discriminante Lineal y Regresión Logística). Los resultados indican un desempeño más elevado en las técnicas de aprendizaje automático. Es más, See5 y Rough Set aportan unos modelos de decisión fácilmente entendibles, e interpretables, lo que demuestra que estos métodos pueden ser útiles para evaluar la insolvencia de empresas de seguros

Data mining reduction methods and performances of rules

In data mining the accuracy of models are associated with the strength of the rules.However, most machine learning techniques produce a large number of rules.The consequence is with large number of rules generated,processing time is much longer. This study examines rules of different lengths of attributes in terms of performance based on percentage of accuracy. The research adopts the Knowledge Discovery in Databases “KDD” methodology for analysis and applies various data mining techniques in the experiments.Data of 50 hardware dataset companies which, contains 31 attributes and 400 records have been used. In summary, results show that in terms of performance of rules, Genetic Algorithm has produced the highest number of rules followed by Johnson’s Algorithm and Holte’s 1R.The best classifier for extracting rules in this study is VOT (Voting of Object Tracking).In terms of performance of rules, best results comes from rules with 30 attributes, followed by rules with 1 intersection attribute and lastly rules with 3 intersection attributes. Among the three sets of attributes, the 3 intersection attributes are considered as the attributes that can be used as predictor attributes

Risk factor selection in automobile insurance policies: a way to improve the bottom line of insurance companies

The objective of this paper is to test the validity of using 'bonus-malus' (BM) levels to classify policyholders satisfactorily. In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed. The empirical evidence shows that common risk factors employed by insurance companies are good explanatory variables for classifying car policyholders' policies. In addition, the BM level variable slightly increases the explanatory power of the a priori risks factors

The efficiency of bankruptcy predictive models - genetic algorithms approach

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe present dissertation evaluates the contribution of genetic algorithms to improve the performance of bankruptcy prediction models. The state-of-the-art points to a better performance of MDA (Multiple Discriminant Analysis)-based models, which, since 1968, are the most applied in the field of bankruptcy prediction. These models usually recur to ratios commonly used in financial analysis. From the comparative study of (1) logistic regression-based models with the forward stepwise method for feature selection, (2) Altman's Z-Score model (Edward I. Altman, 1983) based on MDA and (3) logistic regression with the contribution of genetic algorithms for variable selection, a clear predominance of the efficiency revealed by the former models can be observed. These new models were developed using 1887 ratios generated a posteriori from 66 known variables, derived from the accounting, financial, operating, and macroeconomic analysis of firms. New models are thus presented, which are very promising for predicting bankruptcy in the medium to long term, in the context of increasing instability surrounding firms for different countries and sectors.A dissertação realizada avalia a contribuição dos algoritmos genéticos para melhorar a performance dos modelos de previsão de falência. O estado da arte aponta para uma melhor performance dos modelos baseados em MDA (Análise descriminante multivariada) que por isso, desde de 1968, são os mais aplicados no âmbito da previsão de falência. Estes modelos recorrem habitualmente a rácios comumente utlizados em análise financeira. A partir do estudo comparado de modelos baseados em (1) regressão logística com o método forward stepwise para escolha variáveis, (2) o modelo Z-Score de Edward Altman (1983) baseado em MDA e (3) regressão logística com o contributo de algoritmos genéticos para escolha variáveis, observa-se um claro predomínio da eficácia revelada por estes últimos. Estes novos modelos, agora propostos, foram desenvolvidos com recurso a 1887 rácios gerados a posteriori a partir de 66 variáveis conhecidas, oriundas da análise contabilística, financeira, de funcionamento e de enquadramento macroeconómico das empresas. São assim apresentados novos modelos, muito promissores, para a previsão de falência a médio longo prazo em contexto de crescente instabilidade na envolvente das empresas, para diferentes países e sectores

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000–2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.This work has partially been supported by the Mexican Science and Technology Council (CONACYT-Mexico) through a Postdoctoral Fellowship [223351], the Spanish Ministry of Economy under grant TIN2013-46522-P and the Generalitat Valenciana under grant PROMETEOII/2014/062

Corporate Bankruptcy Prediction

Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy