Consumer credit in comparative perspective

We review the literature in sociology and related fields on the fast global growth of consumer credit and debt and the possible explanations for this expansion. We describe the ways people interact with the strongly segmented consumer credit system around the world—more specifically, the way they access credit and the way they are held accountable for their debt. We then report on research on two areas in which consumer credit is consequential: its effects on social relations and on physical and mental health. Throughout the article, we point out national variations and discuss explanations for these differences. We conclude with a brief discussion of the future tasks and challenges of comparative research on consumer credit.Accepted manuscrip

Forecasting creditworthiness in retail banking: a comparison of cascade correlation neural networks, CART and logistic regression scoring models

The preoccupation with modelling credit scoring systems including their relevance to forecasting and decision making in the financial sector has been with developed countries whilst developing countries have been largely neglected. The focus of our investigation is the Cameroonian commercial banking sector with implications for fellow members of the Banque des Etats de L’Afrique Centrale (BEAC) family which apply the same system. We investigate their currently used approaches to assessing personal loans and we construct appropriate scoring models. Three statistical modelling scoring techniques are applied, namely Logistic Regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN). To compare various scoring models’ performances we use Average Correct Classification (ACC) rates, error rates, ROC curve and GINI coefficient as evaluation criteria. The results demonstrate that a reduction in terms of forecasting power from 15.69% default cases under the current system, to 3.34% based on the best scoring model, namely CART can be achieved. The predictive capabilities of all three models are rated as at least very good using GINI coefficient; and rated excellent using the ROC curve for both CART and CCNN. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies borrower’s account functioning, previous occupation, guarantees, car ownership, and loan purpose as key variables in the forecasting and decision making process which are at the heart of overall credit policy

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Localized Regression

The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. This restriction does not hold if localization is combined with a reduction of dimension. In particular it is shown that localization yields powerful classifiers even in higher dimensions if localization is combined with locally adaptive selection of predictors. A robust localized logistic regression (LLR) method is developed for which all tuning parameters are chosen data¡adaptively. In an extended simulation study we evaluate the potential of the proposed procedure for various types of data and compare it to other classification procedures. In addition we demonstrate that automatic choice of localization, predictor selection and penalty parameters based on cross validation is working well. Finally the method is applied to real data sets and its real world performance is compared to alternative procedures

Credit-Scoring Methods (in English)

The paper reviews the best-developed and most frequently applied methods of credit scoring employed by commercial banks when evaluating loan applications. The authors concentrate on retail loans – applied research in this segment is limited, though there has been a sharp increase in the volume of loans to retail clients in recent years. Logit analysis is identified as the most frequent credit-scoring method used by banks. However, other nonparametric methods are widespread in terms of pattern recognition. The methods reviewed have potential for application in post-transition countries.banking sector, credit scoring, discrimination analysis, pattern recognition, retail loans