Algorithm for the detection of outliers based on the theory of rough sets

Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.This work was performed as part of the Smart University Project (SmartUniversity2014) financed by the University of Alicante

Modelo de RNA para predecir la morosidad de microcredito en la Banca Estatal Peruana

El Banco de la Nación tiene una delicada labor que cumplir ante los problemas sociales y económicos, para ampliar su cobertura a todo el territorio nacional y a todos los ciudadanos. Por ello conviene que aumente radicalmente su competencia tecnológica a fin de adoptar decisiones óptimas. Una de las formas tecnológicas de hacerlo es adoptar un modelo de Red Neuronal Artificial (RNA) que posteriormente será implementado y probado. El presente trabajo de tesis pretende realizar una propuesta sobre un nuevo servicio de Microcrédito, el cual se intenta vincularlo a un aspecto muy puntual de la computación moderna aplicada, donde se muestre que es posible predecir la morosidad de los clientes, planteando un modelo basado en RNA. Este acercamiento innovador también incluirá la metodología de Minería de Datos para proyectos relacionados con redes neuronales artificiales. PALABRAS CLAVES: Microcrédito, Redes Neuronales y Minería de datos.The National Bank has a delicate work to play in social and economic problems, about extending their coverage throughout the national territory and for all citizens. Therefore it is important to increase radically their technological skills so that they know best decisions. One of the technological ways of doing so is adopting a model Artificial Neuronal network (RNA) which will then be implemented and tested. The present thesis work made a proposal on a new service of microcredit, are trying to link it to a very specific aspect of applied modern computing, showing specifically that it is possible predict the delinquency customers, proposing a model based on RNA. This innovative approach also includes the Data Mining methodology for projects relationship with neural networks. KEYWORDS: Microcredit, Artificial Neural Network and Data Mining.Tesi

Basel II compliant credit risk modelling: model development for imbalanced credit scoring data sets, loss given default (LGD) and exposure at default (EAD)

The purpose of this thesis is to determine and to better inform industry practitioners to the most appropriate classification and regression techniques for modelling the three key credit risk components of the Basel II minimum capital requirement; probability of default (PD), loss given default (LGD), and exposure at default (EAD). The Basel II accord regulates risk and capital management requirements to ensure that a bank holds enough capital proportional to the exposed risk of its lending practices. Under the advanced internal ratings based (IRB) approach Basel II allows banks to develop their own empirical models based on historical data for each of PD, LGD and EAD.In this thesis, first the issue of imbalanced credit scoring data sets, a special case of PD modelling where the number of defaulting observations in a data set is much lower than the number of observations that do not default, is identified, and the suitability of various classification techniques are analysed and presented. As well as using traditional classification techniques this thesis also explores the suitability of gradient boosting, least square support vector machines and random forests as a form of classification. The second part of this thesis focuses on the prediction of LGD, which measures the economic loss, expressed as a percentage of the exposure, in case of default. In this thesis, various state-of-the-art regression techniques to model LGD are considered. In the final part of this thesis we investigate models for predicting the exposure at default (EAD). For off-balance-sheet items (for example credit cards) to calculate the EAD one requires the committed but unused loan amount times a credit conversion factor (CCF). Ordinary least squares (OLS), logistic and cumulative logistic regression models are analysed, as well as an OLS with Beta transformation model, with the main aim of finding the most robust and comprehensible model for the prediction of the CCF. Also a direct estimation of EAD, using an OLS model, will be analysed. All the models built and presented in this thesis have been applied to real-life data sets from major global banking institutions