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Keyword

default prediction

Description: Default prediction is the use of statistical or machine-learning methods to estimate whether a borrower or firm will fail to meet credit obligations.
OpenAlex ID: keywords/default-prediction
Statistical Classification Methods in Consumer Credit Scoring: A Review
1997 · D. J. Hand, W. E. Henley · Journal of the Royal Statistical Society Series A (Statistics in Society) · 1,036
Neural network credit scoring models
2000 · David West · Computers & Operations Research · 1,031
The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
2008 · I-Cheng Yeh, Che-hui Lien · Expert Systems with Applications · 873
Predictably Unequal? The Effects of Machine Learning on Credit Markets
2021 · ANDREAS FUSTER, PAUL GOLDSMITH‐PINKHAM, et al. · The Journal of Finance · 754
How did increased competition affect credit ratings?
2011 · Bo Becker, Todd Milbourn · Journal of Financial Economics · 694
Credit Scoring and Its Applications
2002 · Lyn C. Thomas, David B. Edelman, et al. · Society for Industrial and Applied Mathematics eBooks · 667
Screening Peers Softly: Inferring the Quality of Small Borrowers
2015 · Rajkamal Iyer, Asim Ijaz Khwaja, et al. · Management Science · 622
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Modelling Credit Risk for SMEs: Evidence from the U.S. Market
2007 · Edward I. Altman, Gabriele Sabato · Abacus · 606
A comparison of neural networks and linear scoring models in the credit union environment
1996 · Vijay S. Desai, Jonathan N. Crook, et al. · European Journal of Operational Research · 522
The role of non-financial factors in internal credit ratings
2004 · Jens Grunert, Lars Norden, et al. · Journal of Banking & Finance · 438
Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects
2021 · Elena Dumitrescu, Sullivan Hué, et al. · European Journal of Operational Research · 431
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A new fuzzy support vector machine to evaluate credit risk
2005 · Yongqiao Wang, Shouyang Wang, et al. · IEEE Transactions on Fuzzy Systems · 383
Corporate financial distress and bankruptcy : predict and avoid bankruptcy, analyze and invest in distressed debt
2005 · Edward I. Altman, Edith Hotchkiss · 371
A neural network approach for credit risk evaluation
2007 · Eliana Angelini, Giacomo di Tollo, et al. · The Quarterly Review of Economics and Finance · 349
The value of non-financial information in SME risk management
2010 · Edward I. Altman, Gabriele Sabato, et al. · The Journal of Credit Risk · 339
Tiebreaker: Certification and Multiple Credit Ratings
2012 · DION BONGAERTS, K. J. MARTIJN CREMERS, et al. · The Journal of Finance · 332
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Determinants of Default in P2P Lending
2015 · Carlos Serrano-Cinca, Begoña Gutiérrez-Nieto, et al. · PLoS ONE · 326
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Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions
2018 · Yung-Chia Chang, Kuei-Hu Chang, et al. · Applied Soft Computing · 318
Quantifying Credit Risk I: Default Prediction
2003 · Stephen Kealhofer · Financial Analysts Journal · 317
Corporate bond default risk: A 150-year perspective
2011 · Kay Giesecke, Francis A. Longstaff, et al. · Journal of Financial Economics · 311
Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data
1988 · William F. Messier, James V. Hansen · Management Science · 289
Evaluation of credit risk based on firm performance
2009 · Maria Psillaki, Ioannis E. Tsolas, et al. · European Journal of Operational Research · 234
Credit scoring with macroeconomic variables using survival analysis
2008 · T Bellotti, J Crook · Journal of the Operational Research Society · 231
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Default and Recovery Rates of Corporate Bond Issuers: 2000
2001 · David T. Hamilton · SSRN Electronic Journal · 228
An anatomy of rating through the cycle
2003 · Gunter Löffler · Journal of Banking & Finance · 226
Works count: 3,510
Citation count: 33,130
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