Credit Scoring and Its Applications

By Lyn C. Thomas, D. Edelman, Jonathan N. Crook. SIAM monographs on mathematical modeling and computation, 2002

763 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ae10c9dec98ebd8a851845258bc373ad3f0a2a46

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Lyn C. Thomas
2. David Martens
3. Stefan Lessmann
4. Justin Engelmann
5. Hussein A. Abdou
6. John Pointon
7. John Banasik
8. Bart Baesens

In Finance

1. Lyn C. Thomas
2. David Martens
3. Hussein A. Abdou
4. John Pointon
5. Stefan Lessmann
6. Bart Baesens
7. Justin Engelmann
8. John Banasik

Citations in the corpus

• Recent developments in consumer credit risk assessment (2007, background): “Thomas et al. (2002) discuss research questions in credit scoring especially those concerning Basel 2.”
• Does reject inference really improve the performance of application scoring models (2004, methodology): “…of the necessary assumptions in using these techniques with data typically used in credit scoring models has been reviewed by a number of authors (Ash and Meester, 2002; Banasik et al., 2003; Hand and Henley, 1993/4; Joanes, 1993/4; Thomas et al., 2002). weighted in the selection process.”
• Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning (2020, methodology): “We compare the oversampling methods on seven real-world retail credit scoring datasets with sample sizes ranging from small to large n obtained from the UCI Machine Learning Repository [47] [48], previous papers from the credit scoring literature[6] [49], the Kaggle “Give Me Some Credit" competition”
• Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature (2011, methodology): “It should be emphasised that there are other performance evaluation criteria, such as the GINI coefficient, which “gives one number that summarizes the performance of the scorecard over all cut-off scores” (Thomas et al. 2002, p. 116), MSE, RMSE, MAE, and Goodness of Fit test (calibration).”
• Corporate residence fraud detection (2014, methodology): “To deal with the many-to-one variables, such as location, which appear in the transaction data, we encode them in the structured data via a “weight-of-evidence” encoding; this is a logarithmic transformation that allows one to transform a categorical variable into a variable that is monotonically re”
• Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015, methodology): “= 1: 2, … , 1: 50 , always assuming that it is more costly to grant credit to a bad risk than rejecting a good application (e.g., Thomas, et al., 2002).”
• Inferring the outcomes of rejected loans: an application of semisupervised clustering (2019, background): “These measures of model performance are common in credit scoring, and details can be found in Thomas et al. (2017).”