Support vector machines for credit scoring and discovery of significant features

By Tony Bellotti, Jonathan N. Crook. Expert systems with applications, 2009

343 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Finance

View Paper: https://www.semanticscholar.org/paper/73e25066a49472e3b5e545b6a0ea4bb1b66118be

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Citations in the corpus

Machine Learning in Banking Risk Management: A Literature Review (2019, methodology): “The SVM has been applied as is or in some varied form to design a credit risk evaluation and credit scoring models (Bellotti and Crook 2009; Cao et al. 2013; Van Gestel et al. 2003; Huang et al. 2007; Lai et al. 2006).”
On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial Services (2018, background): “The respective themes include: the economics of electronic banking strategy and shared ATM networks [30, 87]; process variation as a basis for service quality and performance [58]; retail banking strategy when electronic distribution technology costs influence bank competition and performance [23]; ”
Inferring the outcomes of rejected loans: an application of semisupervised clustering (2019, background): “Equivalently, the probability of default can be calculated by The SVM is a machine learning classiﬁer that has recently become popular in credit scoring (Bellotti and Crook, 2009; Huang et al. , 2007; Li et al. , 2017).”
Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature (2011, methodology): “…neighbour, fuzzy rules, kernel learning method, Markov models and linear programming, have been discussed in the literature (see for example: Bellotti & Crook, 2009; Elliott & Filinkov, 2008; Crook et al., 2007; Hoffmann et al., 2007; Huang et al., 2007; Baesens et al., 2003; Yang, 2007;…”
A hybrid deep learning model for consumer credit scoring (2018, methodology): “[2] Crook, J.N., Edelman, D.B., and Thomas, L.C., 2007.”
Consumer Credit Risk Models Via Machine-Learning Algorithms (2010, background): “7See, for example, Li, Shiue, and Huang (2006) and Bellotti and Crook (2009) for applications of machine learning based model to consumer credit.”
A deep learning approach for credit scoring using credit default swaps (2017, background): “Bellotti and Crook (2009) test support vector machines against several other well-known algorithms on a large credit card database and find that SVMs are successful in comparison to established approaches to classifying credit card customers who default.”