Statistical Classification Methods in Consumer Credit Scoring: a Review

By David J. Hand, W. Henley. 1997

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Manipulating and Measuring Model Interpretability (2018, background): “1 INTRODUCTION Machine learning models are increasingly used to aid decision making in high-stakes domains, such as medical diagnosis [57], credit risk assessment [43], judicial sentencing and bail [5, 18, 48, 53], and hiring [66].”
A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers (2000, background): “This area of financial risk also has a limited literature with only a few surveys ( Rosenberg and Gleit 1994, Hand and Henley 1997, Thomas 1992, Thomas 1998) and a handful of books ( Hand and Jacka 1998, Thomas Crook Edelman 1992, Lewis 1992, Mays 1998).”
Credit Scoring and the Availability, Price, and Risk of Small Business Credit (2002, unknown): “See Hand and Henley (1997) for a review of the statistical methods used for consumer credit scoring.”
Integrating induction and deduction for finding evidence of discrimination (2009, background): “For instance, data mining and machine learning classification models are constructed on the basis of historical data exactly with the purpose of learning the distinctive elements of different classes, such as good/bad debtor in credit/insurance scoring systems [3, 10, 27] or good/bad worker in perso”
A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017, background): “However, NN is criticized as a black box (Hand & Henley, 1997).”
Statistical fraud detection: A review (2002, background): “Traditional credit scorecards (Hand and Henley, 1997) are used to detect customers who are likely to default, and the reasons for this may include fraud.”
Online Deep Learning: Growing RBM on the fly (2018, unknown): “Several studies have employed batch machine learning techniques for credit scoring ( [1], [11], [6], [18]).”
Financial credit risk assessment: a recent review (2015, methodology): “…1 Recent reviews on financial credit risk assessment Year Technique Work 1996 Statistical methods Dimitras et al. (1996) 1997 Statistical methods Hand and Henley (1997) 1997 Neural networks Wong et al. (1997) 1998 Neural networks Wong and Selvi (1998) 1999 Neural networks Vellido et al.…”
Machine Learning in Financial Crisis Prediction: A Survey (2012, background): “of bankruptcy-prediction and credit-scoring models since the 1990s [1], [35], [44], [169].”
Predictive Modeling of Customer Repayment for Sustainable Pay-As-You-Go Solar Power in Rural India (2015, background): “Credit scoring is one of the early successful examples of predictive analytics and has been primarily approached through supervised machine learning techniques [5], [6].”
Classifier Technology and the Illusion of Progress (2006, unknown): “Changes to the distributions that describe the customers explain why, in the credit scoring and banking industries [16, 20, 39, 42], the classiﬁcation rules used to predict which applicants are likely to default on loans are updated every few months: their performance degrades, not because the…”
Recent developments in consumer credit risk assessment (2007, background): “Rosenberg and Gleit (1994) review different types of classifiers, (Hand and Henley, 1997) cover earlier results concerning the performance of classifiers whilst (Thomas, 2000) additionally reviews behavioural and profit scoring.”
Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates (2018, background): “To some extent, any small improvement for the credit evaluation model would produce great profits for banks [15].”
Small Business Credit Scoring and Credit Availability* (2007, unknown): “Limited dependent variable estimation techniques, such as logit and probit, were then generally used to analyze the BERGER AND FRAME 7 3See Hand and Henley (1997) for a detailed overview of consumer credit scoring, including the treatment of various data and statistical issues regularly encountered…”
Rules versus discretion in loan rate setting (2010, unknown): “In other words, we replicate a multivariate logistic credit-scoring model.44 Studies focusing on scoring methods suggest that financial institutions extensively use multivariate logistic models to estimate default probabilities (Altman and Saunders (1998), Hand and Henley (1997)).”