A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers

By Lyn C. Thomas. 2000

834 citations recorded by the source corpus.

Subfields: Accounting, Management Science and Operations Research, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/174422f763ff53cf5e9feeb5f42b0c29eb17ed84

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Citations in the corpus

Forecasting and operational research: a review (2008, methodology): “This will require either a periodic re-estimation of the static model or incorporating changing circumstances through explanatory economic indicators that capture populations with drift (Thomas, 2000).”
Financial credit risk assessment: a recent review (2015, methodology): “Section 3 focuses on the recently emerged multiple classier systems which have considerable potential for performance improvement of individual prediction models.”
A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines (2005, methodology): “In response, the statistical methods, non-parametric methods, and artificial intelligence approaches have been proposed to support the credit approval decision process (Desai, Crook, & Overstreet, 1996; Lee, Chiu, Lu, & Chen, 2002; Thomas, 2000; West, 2000).”
A review of trade credit literature: Opportunities for research in operations (2013, methodology): “readily available (Rosenberg and Gleit, 1994; Thomas, 2000; Crook et al., 2007).”
Credit scoring using the hybrid neural discriminant technique (2002, methodology): “In response, the statistical methods, nonparametric statistical methods, and artificial intelligence approaches have been proposed to support the credit decision (Thomas, 2000).”
A Two-step Method to Construct Credit Scoring Models with Data Mining Techniques (2006, methodology): “Until recently, the decision to grant credit was based on human judgment to assess the risk of default [Thomas, 2000].”
Predicting mortgage default using convolutional neural networks (2018, methodology): “Thomas (2000) and Kennedy et al. (2013). The methodology proposed in this paper may be used both for application and behavioral scoring.”
Deep generative models for reject inference in credit scoring (2019, methodology): “The tools that help banks with the ﬁrst problem are called credit scoring models, while behavioral scoring models are used to handle exiting customers [22].”
A literature review on the application of evolutionary computing to credit scoring (2013, background): “…ﬁnance, accounting, human resources, marketing, production, and distribution (Dimitras et al , 1996; Hand and Henley, 1997; Refenes et al , 1997; Wong et al , 1997; Vellido et al , 1999; Thomas, 2000; Metaxiotis and Psarras, 2004), but to the best of our knowledge not speciﬁcally to credit scoring.”
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.”
Determinants of Default in P2P Lending (2015, methodology): “Credit history is another key issue in consumer credit scoring [52].”
Access to Rural Credit Markets in Developing Countries, the Case of Vietnam: A Literature Review (2019, background): “That means credit sources are li ited and the suppliers have to choose their potential customers based on the possibility of default [19,26,27].”