The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant ...

This article explores credit scoring systems as a tool used by the credit industry to evaluate consumers' credit applications and creditworthiness within the context of the EU. After an analysis of the technologies and techniques behind the scoring of individuals, it investigates the most relevant issues behind the reporting of consumer financial information, i.e. the prejudicial side of sharin...

The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons...

Les accords dits « Bâle 2 » sur la solvabilité des banques ont remis au goût du jour les techniques de scoring en imposant aux banques de calculer des probabilités de défaut et le montant des pertes en cas de défaut. Nous présentons dans cet exposé les principales techniques utilisées et les problèmes actuels. Le terme credit scoring désigne un ensemble d’outils d’aide à la décision utilisés pa...

Credit scoring methods aim to assess credit worthiness of potential borrowers to keep the risk of credit loss low and to minimize the costs of failure over risk groups. Standard parametric approaches as logistic discrimination analysis assume that the probability of belonging to the group of ”bad” clients is given by P (Y = 1|X) = F (βX), with Y = 1 indicating a ”bad” client and X denoting the ...

We present a methodology to grant and follow-up credits for micro-entrepreneurs. This segment of grantees is very relevant for many economies, especially in developing countries, but shows a behaviour different to that of classical consumers where established credit scoring systems exist. Parts of our methodology follow a proven procedure we have applied successfully in several credit scoring p...

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal...

This thesis has investigated two-stage regularized logistic regressions applied on the credit scoring problem. Credit scoring refers to the practice of estimating the probability that a customer will default if given credit. The data was supplied by Klarna AB, and contains a larger number of observations than many other research papers on credit scoring. In this thesis, a two-stage regression r...

This paper investigates the credit scoring accuracy of "ve neural network models: multilayer perceptron, mixture-of-experts, radial basis function, learning vector quantization, and fuzzy adaptive resonance. The neural network credit scoring models are tested using 10-fold crossvalidation with two real world data sets. Results are benchmarked against more traditional methods under consideration...