Cutpoint Selection via Penalization in Credit Scoring

Sample selection bias in credit scoring models

Sample selection in credit-scoring models

We examine three models for sample selection that are relevant for modeling credit scoring by commercial banks. A binary choice model is used to examine the decision of whether or not to extend credit. The selectivity aspect enters because such models are based on samples of individuals to whom credit has already been given. A regression model with sample selection is suggested for predicting e...

Credit scoring and the sample selection bias

For creating or adjusting credit scoring rules, usually only the accepted applicant’s data and default information are available. The missing information for the rejected applicants and the sorting mechanism of the preceding scoring can lead to a sample selection bias. In other words, mostly inferior classification results are achieved if these new rules are applied to the whole population of a...

Credit scoring in banks and financial institutions via data mining techniques: A literature review

This paper presents a comprehensive review of the works done, during the 2000–2012, in the application of data mining techniques in Credit scoring. Yet there isn’t any literature in the field of data mining applications in credit scoring. Using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the Science direct onli...

Using DEA for Classification in Credit Scoring

Credit scoring is a kind of binary classification problem that contains important information for manager to make a decision in particularly in banking authorities. Obtained scores provide a practical credit decision for a loan officer to classify clients to reject or accept for payment loan. For this sake, in this paper a data envelopment analysis- discriminant analysis (DEA-DA) approach is us...