M
María Jesús Segovia-Vargas
Researcher at Complutense University of Madrid
Publications - 36
Citations - 413
María Jesús Segovia-Vargas is an academic researcher from Complutense University of Madrid. The author has contributed to research in topics: Audit & Computer science. The author has an hindex of 8, co-authored 31 publications receiving 286 citations. Previous affiliations of María Jesús Segovia-Vargas include University of Tartu & University of Alcalá.
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Journal ArticleDOI
Genetic programming for the prediction of insolvency in non-life insurance companies
Sancho Salcedo-Sanz,José-Luis Fernández-Villacañas,María Jesús Segovia-Vargas,Carlos Bousoño-Calzón +3 more
TL;DR: The final purpose is to create an automatic diagnostic system for analysing non-insurance firms using their financial ratios as explicative variables using genetic programming (GP), a class of evolutionary algorithms.
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Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending
TL;DR: This work assesses the well-known logistic regression model and several machine learning algorithms for granting scoring in P2P lending and reveals that the machine learning alternative is superior in terms of not only classification performance but also explainability.
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Which Characteristics Predict the Survival of Insolvent Firms? An SME Reorganization Prediction Model
TL;DR: In this article, the authors study the characteristics of bankrupt firms to achieve a preventive diagnosis for reorganization by means of artificial intelligence (AI) methodologies such as rough set and PART methods.
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Hybridizing logistic regression with product unit and RBF networks for accurate detection and prediction of banking crises
Pedro Antonio Gutiérrez,María Jesús Segovia-Vargas,Sancho Salcedo-Sanz,César Hervás-Martínez,A. Sanchis,Jose A. Portilla-Figueras,Francisco Fernández-Navarro +6 more
TL;DR: A novel model for detection and prediction of crises is introduced, based on the hybridization of a standard logistic regression with product unit (PU) neural networks and radial basis function (RBF) networks, which performs better than other existing statistical and artificial intelligence methods in this problem.
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Evaluating the Internationalization Success of Companies Through a Hybrid Grouping Harmony Search—Extreme Learning Machine Approach
Itziar Landa-Torres,E. G. Ortiz-Garcia,Sancho Salcedo-Sanz,María Jesús Segovia-Vargas,Sergio Gil-Lopez,M. Miranda,J.M. Leiva-Murillo,J. Del Ser +7 more
TL;DR: A novel hybrid soft-computing approach for evaluating the internationalization success of a company based on existing past data is presented and is shown to be satisfactory in comparison with alternative state-of-the-art techniques.