Credit scoring and the sample selection bias

The impact of sample bias on consumer credit scoring performance and profitability

This article seeks to gain insight into the influence of sample bias in a consumer credit scoring model. Considering the vital implications on revenues and costs concerned with the issuing and repayment of commercial credit, predictive performance of the model is crucial, and sample bias has been suggested to pose a sizeable threat to profitability due to its implications on either population d...

Sample selection bias in credit scoring models

The Economic Value of Reject Inference in Credit Scoring

We use data with complete information on both rejected and accepted bank loan applicants to estimate the value of sample bias correction using Heckman’s two-stage model with partial observability. In the credit scoring domain such correction is called reject inference. We validate the model performances with and without the correction of sample bias by various measurements. Results show that it...

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...

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...