The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation

By Ray H. Anderson. 2007

344 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1b151ff2c3a76fe70b4ea687e0fe8e3d8556842c

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Citations in the corpus

Adaptive Machine Learning for Credit Card Fraud Detection (2015, background): “Credit card frauds may occur in various ways [9]: just to mention some, we can have stolen card fraud, cardholder-not-present fraud and application fraud: • Stolen card fraud is the most common type of fraud where the fraudster usually tries to spend as much as possible and as quickly as possible.”
Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature (2011, background): “…a logical future development of the subjective credit ratings (see for example, Beynon, 2005) first provided by nineteenth century credit bureaux, that has been driven by a need for objective , fast and consistent decisions, and made possible by advances in technology” (Anderson, 2007, p. 6).”
Deep generative models for reject inference in credit scoring (2019, methodology): “tive Models, Credit Scoring, Semi-Supervised Learning 1 Introduction Credit scoring uses statistical models to transform the customers’ data into a measure of the borrowers’ ability to repay the loan [2]. These models are developed, commonly, based on accepted applications because the bank knows whe”
Inferring the outcomes of rejected loans: an application of semisupervised clustering (2019, methodology): “Extrapolation is by polarized parcelling in Anderson (2007), and both polarized and fuzzy parcelling are associated with extrapolation approaches, which have a strong assumption that the same set of information is available for both old and new scorecards.”