Consumer credit scoring and credit risk management have been the core research problem in financial industry for decades. In this paper, we target at inferring this particular user attribute called credit, i.e., whether a user is of the good credit class or not, from online social data. However, existing credit scoring methods, mainly relying on financial data, face severe challenges when tackl...

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It has become crucial over the years for nations to improve their credit scoring methods and techniques in light of the increasing volatility of the global economy. Statistical methods or tools have been the favoured means for this; however artificial intelligence or soft computing based techniques are becoming increasingly preferred due to their proficient and precise nature and relative simpl...

In this paper, we set out to compare several techniques that can be used in the analysis of imbalanced credit scoring data sets. In a credit scoring context, imbalanced data sets frequently occur as the number of defaulting loans in a portfolio is usually much lower than the number of observations that do not default. As well as using traditional classification techniques such as logistic regre...

Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a m...

Lenders experience positive net revenue impacts from lending if they increase the classification power of their credit scoring systems. If loan officers’ subjective assessments of otherwise intangible borrower characteristics contain additional information about a borrower, a lender may improve the default forecast quality of his internal credit scoring systems by utilizing this subjective info...

We generalize an empirical likelihood approach to missing data to the case of consumer credit scoring and provide a Hausman test for nonignorability of the missings. An application to recent consumer credit data shows that our model yields parameter estimates which are significantly different (both statistically and economically) from the case where customers who were refused credit are ignored.