Modelos y metodologías de credit score para personas naturales: una revisión literaria
30 Jun 2017-Vol. 3, Iss: 5, pp 13-28
TL;DR: In this paper, a literature review on risk scoring models for credit granting in personal banking is provided, with an up-to-date list supported by scholars and experts in the field.
Abstract: This paper provides a literature review on risk scoring models for credit granting in personal banking The methods by Abdou & Pointon (2011), Glennon, Kiefer, Larson, & Choi (2008), and Saavedra-Garcia (2010) are considered The aim is to create a sorting scheme to explain the multiple mathematical and econometrical models used for credit scoring and to produce an up-to-date list supported by scholars and experts in the field
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TL;DR: A CHAID-based approach to detecting classification accuracy heterogeneity across segments of observations is proposed and it was shown that different segments may have absolutely different churn predictors, therefore such a partitioning gives a better insight into factors influencing customer behavior.
Abstract: In this study a CHAID-based approach to detecting classification accuracy heterogeneity across segments of observations is proposed. This helps to solve some important problems, facing a model-builder: 1. How to automatically detect segments in which the model significantly underperforms? 2. How to incorporate the knowledge about classification accuracy heterogeneity across segments to partition observations in order to achieve better predictive accuracy? The approach was applied to churn data from the UCI Repository of Machine Learning Databases. By splitting the dataset into 4 parts, which are based on the decision tree, and building a separate logistic regression scoring model for each segment we increased the accuracy by more than 7 percentage points on the test sample. Significant increase in recall and precision was also observed. It was shown that different segments may have absolutely different churn predictors. Therefore such a partitioning gives a better insight into factors influencing customer behavior.
24 citations
23 Apr 2020
TL;DR: In this article, the authors developed a logit model with interactions per country and sector, which makes it possible to find the probability of improving the Tobin's Q when the MILA corporate governance index (MCGI) rises.
Abstract: In the literature, the relation between corporate governance and enterprise value has been extensively studied, and has been found to be positive. However, it is assumed that the impact of improvements in corporate governance practices and enterprise value is homogeneous for any company. We developed a logit model with interactions per country and sector which makes it possible to find the probability of improving the Tobin’s Q when the MILA corporate governance index (MCGI) rises. We found statistically significant evidence that the probabilities differ for the sectors and countries studied.
2 citations
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TL;DR: In this paper, a credit scoring model is developed for a Tunisian Microfinance Bank by applying fuzzy classifiers where the fuzzy knowledge bases are optimized through differential evolution, and the obtained results reveal that the proposed model consistently gives a better average correct classification rate than the decision tree model.
Abstract: The process of effective credit risk assessment plays an important role in the financial decision making in Microfinance Institutions (MFIs) as it enables faster credit approval decisions and diminishes the possible risks associated with customers’ repayment defaults. Credit scoring is the most commonly used technique for evaluating the creditworthiness of loans which has gradually begun to find its way into the microfinance field. Many parametric and nonparametric techniques have been adopted by financial institutions to develop accurate credit scoring models. In this study, a credit scoring model is developed for a Tunisian Microfinance Bank by applying fuzzy classifiers where the fuzzy knowledge bases are optimized through differential evolution. Further, the performance of the proposed model is compared to that of the decision tree model. The obtained results reveal that the proposed model consistently gives a better average correct classification rate than the decision tree model. As with the decision tree model, the proposed model can be easily understood by any user and is very useful in the context of credit evaluation process, since it is in ‘if-then’ rule form; unlike decision tree model, the proposed model does not stay in a black box. In the proposed model, the interpretation of independent variables may provide valuable information for bankers and consumers, especially in explaining why credit applications are rejected.
2 citations
03 Jan 2023
TL;DR: In this paper , the authors presented a gender-sensitive risk analysis model using artificial intelligence (AI) within the framework of complex thinking, specifically for impoverished and vulnerable women, in order to detect patterns in women's behaviors and attitudes in the venture start-up process.
Abstract: Abstract The objective is to present a proposal for a gender-sensitive risk analysis model using artificial intelligence (AI) within the framework of complex thinking, specifically for impoverished and vulnerable women. This international non-parametric study analyzed business and entrepreneurial activity in a sample of 2,787 women. The methodological design included data analysis, the postulation of a proposed model, and a validation method for the credit risk analysis model. There is a correlation between the level of schooling of impoverished and vulnerable women with the possibility of self-employment and selling a product or service. In the framework of complex thinking, the perception of innovative thinking is related to the level of education and innovative decision-making in professional projects. Women with a higher level of schooling tend to think about their professional projects systematically. Promoting complex thinking involves innovative educational practices to encourage critical, systemic, scientific, and innovative thinking in entrepreneurship and sustainable development. Integrating reasoning for complexity benefits women and contributes to economic and social growth in vulnerable regions. In contrast to other models, our credit risk analysis model uses AI and variables for gender, vulnerability, and complex thinking to detect patterns in women's behaviors and attitudes in the venture start-up process.
References
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14,009 citations
"Modelos y metodologías de credit sc..." refers background or methods in this paper
...Modelos LDA (Linear Discriminant Analysis) - Modelo Z – Score De la misma forma que Fisher (1936) obtuvo el proceso discriminante, estos modelos fueron usados por Altman (1968), Altman (1980) y Elliott, Siu & Fung (2014) en sus estudios para identificar en el primer caso la probabilidad de quiebra…...
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...Modelos paramétricos multivariantes Análisis discriminante Fisher (1936), Puertas & Marti (2012) y Mures, García, & Vallejo (2011), explican que el análisis discriminante es una relación de comportamiento de múltiples variables identificadas frente a una variable dependiente, la cual estructura…...
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...Estas metodologías nacieron con el análisis lineal discriminante (LDA) descritos por Fisher (1936) como una variante de los modelos ANOVA y que fueron la entrada a los modelos de identificación de quiebra, y a través de los años han tomado vertientes de uso con modelos lineales, no lineales,…...
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TL;DR: Results demonstrate that the multilayer perceptron may not be the most accurate neural network model, and that both the mixture-of-experts and radial basis function neural network models should be considered for credit scoring applications.
959 citations
"Modelos y metodologías de credit sc..." refers background in this paper
...…usados con procesos de nodos o redes que asemejan al cerebro, encontrándose: Redes Neuronales (Neuronal networks) Desai, Crook & Overstreet (1996);West (2000);Esteve (2007); Pérez & Fernández (2007) y Soydaner & Kocadağlı (2015), explican que el uso de las redes neuronales utilizan un sistema…...
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Book•
16 Aug 2017TL;DR: Lyn C. Thomas as discussed by the authors is a Professor of Management Science at the University of Southampton, UK and David B. Edelman is Credit Director of Royal Bank of Scotland, Edinburgh.
Abstract: From the Publisher:
About the Author
Lyn C. Thomas is a Professor of Management Science at the University of Southampton. Jonathan N. Crook is Reader in Business Economics at the University of Edinburgh. David B. Edelman is Credit Director of Royal Bank of Scotland, Edinburgh.
585 citations
TL;DR: The results indicate that customized neural networks offer a very promising avenue for building effective generic models if the measure of performance is percentage of bad loans correctly classified, but logistic regression models are comparable to the neural networks approach.
523 citations
01 Jan 2000
TL;DR: From the Publisher: About the Author Lyn C. Thomas is a Professor of Management Science at the University of Southampton and David B. Edelman is Credit Director of Royal Bank of Scotland, Edinburgh.
Abstract: From the Publisher:
About the Author
Lyn C. Thomas is a Professor of Management Science at the University of Southampton. Jonathan N. Crook is Reader in Business Economics at the University of Edinburgh. David B. Edelman is Credit Director of Royal Bank of Scotland, Edinburgh.
461 citations