Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Many benchmarking studies have illustrated that complex analytical techniques only provide marginal performance gains on structured, tabular data sets such as fraud detection, credit scoring and marketing analytics.
“Many benchmarking studies have illustrated that complex analytical techniques only provide marginal performance gains on structured, tabular data sets as frequently encountered in common classification tasks such as fraud detection, credit scoring and marketing analytics (Baesens et al., 2003; Lessmann et al., 2015).”
From Data engineering for fraud detection (2021) · cites this paper as Lessmann et al., 2015
In §5.1 (page unavailable), the paper explicitly rejects the inference that complex classifiers offer little advantage over simpler methods, finding that some advanced methods significantly outperform logistic regression in credit scoring. It also finds that sophistication does not reliably improve accuracy; its conclusions are limited to credit scoring, not fraud detection or marketing analytics. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe result that random forests outperform logistic regression is consistent with benchmarking experiments performed across various business domains reported in the literature.
“This result is unsurprising and is fully consistent with the results of benchmarking experiments performed across various business domains and reported in the literature [2, 17].”
From Why you should stop predicting customer churn and start using uplift models (2021) · cites this paper as [2]
Section 5.2 reports that logistic regression predicted significantly less accurately than the other selected classifiers, including random forests. But the paper benchmarks retail credit scoring across eight datasets, not experiments across various business domains; that broader scope is unsupported by this source. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSupport vector machines and artificial neural networks are better-suited to handle continuous covariates, which occur frequently in financial time series forecasting.
“One of the explanations for their popularity for time series forecasting (cf. other advanced techniques such as bagged or boosted decision trees), is that they are better-suited to handle continuous covariates (Lessmann, Baesens, Seow, & Thomas, 2015), which occur frequently in financial time series forecasting (e.g., prices, price differences or technical indicators).”
From Bridging the divide in financial market forecasting: machine learners vs. financial economists (2016) · cites this paper as Lessmann, Baesens, Seow, & Thomas, 2015
Section 4.3 (page not available) says ANNs and SVMs benefit when nominal variables are encoded as numbers, while classification trees can work with mixed nominal and numeric data. This supports a preference for numeric inputs, but does not establish that ANNs and SVMs handle continuous covariates better than bagged or boosted trees. (LLM, conf=0.82)
Experimental — accuracy not yet measuredComplex analytical models such as random forests, boosting methods, support vector machines, neural networks and deep learning do not always significantly outperform simple analytical models such as logistic regression.
“We would also like to note that these complex models not always significantly outperform simple analytical models such as logistic regression (Baesens et al., 2003; Lessmann et al., 2015)”
From Data engineering for fraud detection (2021) · cites this paper as Lessmann et al., 2015
Section 5.1 (page unavailable) says sophisticated methods do not necessarily improve accuracy and reports that dynamic ensembles performed less accurately than simpler alternatives, including logistic regression. That supports the qualified point for the credit-scoring classifiers studied, but the paper's listed algorithms do not include deep learning, so it does not establish the claim for every example named. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879 · see citing works
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285 · see citing works
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 11,966 · see citing works
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,869 · see citing works
Experiments with a new boosting algorithm
Freund, Y., & Schapire, R. E. (1996) · Cited by 8,189 · see citing works
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067 · see citing works
LIBLINEAR: A Library for Large Linear Classification
Fan, R.-E., Chang, K., Hsieh, C., Xiang-rui, W., & Lin, C. (2008) · doi:10.5555/1390681.1442794 · Cited by 6,680 · see citing works
Extreme Learning Machine for Regression and Multiclass Classification
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,564 · see citing works
Combining Pattern Classifiers: Methods and Algorithms
Bagui, S. (2005) · Technometrics · doi:10.1198/tech.2005.s320 · Cited by 4,364 · see citing works
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
Dietterich, T. G. (1998) · Neural Computation · doi:10.1162/089976698300017197 · Cited by 3,840 · see citing works
An empirical comparison of supervised learning algorithms
Caruana, R., & Niculescu-Mizil, A. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143865 · Cited by 2,780 · see citing works
Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes
Huang, G.-B., Chen, L., & Siew, C. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875977 · Cited by 2,614 · see citing works
García, S., Fernández, A., Luengo, J., & Herrera, F. (2009) · Information Sciences · doi:10.1016/j.ins.2009.12.010 · Cited by 2,236 · see citing works
Breiman, L. (1996) · Machine Learning · doi:10.1023/a:1018046112532 · Cited by 1,966 · see citing works
Ensembling neural networks: Many could be better than all
Zhou, Z.-H., Wu, J., & Tang, W. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00190-x · Cited by 1,964 · see citing works
Zhang, B., Li, C., & Lin, N. (2020) · De Gruyter eBooks · doi:10.1515/9783110595567-011 · Cited by 1,939 · see citing works
Extreme learning machines: a survey
Huang, G.-B., Wang, D. H., & Lan, Y. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0019-y · Cited by 1,903 · see citing works
Advances in Large-Margin Classifiers
(2000) · The MIT Press eBooks · doi:10.7551/mitpress/1113.001.0001 · Cited by 1,902 · see citing works
Rotation Forest: A New Classifier Ensemble Method
Rodríguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.211 · Cited by 1,897 · see citing works
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Hall, M. A. (2000) · Cited by 1,713 · see citing works
García, S., & Herrera, F. (2008) · Cited by 1,277 · see citing works
Lin, M., Prabhala, N. R., & Viswanathan, S. (2012) · Management Science · doi:10.1287/mnsc.1120.1560 · Cited by 1,272 · see citing works
Landwehr, N., Hall, M., & Frank, E. (2005) · Machine Learning · doi:10.1007/s10994-005-0466-3 · Cited by 1,218 · see citing works
Measuring classifier performance: a coherent alternative to the area under the ROC curve
Hand, D. (2009) · Machine-mediated learning · doi:10.1007/s10994-009-5119-5 · Cited by 1,116 · see citing works
Large margin classification using the perceptron algorithm
Freund, Y., & Schapire, R. E. (1998) · Conference on Learning Theory · doi:10.1145/279943.279985 · Cited by 1,091 · see citing works
Ensemble selection from libraries of models
Caruana, R., Niculescu-Mizil, A., Crew, G., & Ksikes, A. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015432 · Cited by 1,089 · see citing works
Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review
Ravi Kumar, P., & Ravi, V. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.08.043 · Cited by 1,074 · see citing works
Statistical Classification Methods in Consumer Credit Scoring: A Review
Hand, D. J., & Henley, W. E. (1997) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.1997.00078.x · Cited by 1,042 · see citing works
Netlab: Algorithms for Pattern Recognition
Nabney, I. T. (2002) · Cited by 982 · see citing works
A survey of multiple classifier systems as hybrid systems
Woźniak, M., Graña, M., & Corchado, E. (2013) · Information Fusion · doi:10.1016/j.inffus.2013.04.006 · Cited by 909 · see citing works
Benchmarking state-of-the-art classification algorithms for credit scoring
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2601545 · Cited by 904 · see citing works
Self-citationDuygun, M., & Pasiouras, F. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.08.003 · Cited by 811 · see citing works
Credit scoring with a data mining approach based on support vector machines
Huang, C.-L., Chen, M., & Wang, C.-J. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.07.007 · Cited by 809 · see citing works
An experimental comparison of classification algorithms for imbalanced credit scoring data sets
Brown, I., & Mues, C. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.09.033 · Cited by 768 · see citing works
Classifier Technology and the Illusion of Progress
Hand, D. J. (2006) · Statistical Science · doi:10.1214/088342306000000060 · Cited by 723 · see citing works
The Alternating Decision Tree Learning Algorithm
Freund, Y., & Mason, L. (1999) · Cited by 711 · see citing works
Credit Scoring and Its Applications
Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898718317 · Cited by 674 · see citing works
Self-citationConstruction and Assessment of Classification Rules
Wade, M. R., & Hand, D. J. (1999) · Technometrics · doi:10.2307/1270579 · Cited by 621 · see citing works
Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation
Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003) · Management Science · doi:10.1287/mnsc.49.3.312.12739 · Cited by 536 · see citing works
Self-citationUsing neural network ensembles for bankruptcy prediction and credit scoring
TSAI, C., & Wu, J. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.05.019 · Cited by 517 · see citing works
Margineantu, D. D., & Dietterich, T. G. (1997) · Cited by 514 · see citing works
A comparative assessment of ensemble learning for credit scoring
Wang, G., Hao, J., Ma, J., & Jiang, H. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.06.048 · Cited by 504 · see citing works
Experiment with a new boosting algorithm
Freund, Y. (1996) · Medical Entomology and Zoology · Cited by 486 · see citing works
Recent developments in consumer credit risk assessment
Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.09.100 · Cited by 473 · see citing works
Self-citationVerbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.09.031 · Cited by 462 · see citing works
Self-citationComprehensible credit scoring models using rule extraction from support vector machines
Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.04.051 · Cited by 448 · see citing works
Self-citationFrom dynamic classifier selection to dynamic ensemble selection
Ko, A. H. R., Sabourin, R., & Britto, A. S. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.10.015 · Cited by 444 · see citing works
An analysis of diversity measures
Tang, E. K., Suganthan, P. N., & Yao, X. (2006) · Machine Learning · doi:10.1007/s10994-006-9449-2 · Cited by 402 · see citing works
LEE, T., & Chen, I.-F. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2004.12.031 · Cited by 389 · see citing works
Lee, T., Chiu, C.-C., Chou, Y.-C., & Lu, C.-J. (2004) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.11.006 · Cited by 378 · see citing works
Building credit scoring models using genetic programming
ONG, C., Huang, J., & Tzeng, G. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.01.003 · Cited by 360 · see citing works
Support vector machines for credit scoring and discovery of significant features
Bellotti, A. G., & Crook, J. N. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.01.005 · Cited by 340 · see citing works
Neural network ensemble strategies for financial decision applications
West, D., Dellana, S. A., & Qian, J. (2004) · Computers & Operations Research · doi:10.1016/j.cor.2004.03.017 · Cited by 337 · see citing works
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
Martínez-Muñoz, G., Hernández-Lobato, D., & Suárez, A. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.78 · Cited by 317 · see citing works
Classifier ensembles: Select real-world applications
Oza, N. C., & Tumer, K. (2007) · Information Fusion · doi:10.1016/j.inffus.2007.07.002 · Cited by 311 · see citing works
Credit risk assessment with a multistage neural network ensemble learning approach
YU, L., Wang, S., & Lai, K. K. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.01.009 · Cited by 309 · see citing works
Credit Scoring and Its Applications
Thomas, L. C., Crook, J. N., & Edelman, D. B. (2017) · Cited by 308 · see citing works
Self-citationAn experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
Nanni, L., & Lumini, A. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.01.018 · Cited by 295 · see citing works
Trust region Newton methods for large-scale logistic regression
Lin, C., Weng, R. C., & Keerthi, S. S. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273567 · Cited by 287 · see citing works
Evaluating consumer loans using neural networks
Malhotra, R., & Malhotra, D. K. (2003) · Omega · doi:10.1016/s0305-0483(03)00016-1 · Cited by 282 · see citing works
Survival Analysis Methods for Personal Loan Data
Stepanova, M., & Thomas, L. C. (2002) · Operations Research · doi:10.1287/opre.50.2.277.426 · Cited by 266 · see citing works
Self-citationAkkoç, S. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2012.04.009 · Cited by 245 · see citing works
Credit scoring with macroeconomic variables using survival analysis
Bellotti, A. G., & Crook, J. N. (2008) · Journal of the Operational Research Society · doi:10.1057/jors.2008.130 · Cited by 238 · see citing works
Khashman, A. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.02.101 · Cited by 237 · see citing works
Evaluation of credit risk based on firm performance
Psillaki, M., Tsolas, I. E., & Margaritis, D. (2010) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.03.032 · Cited by 234 · see citing works
Support vector machines for default prediction of SMEs based on technology credit
Kim, H., & Sohn, S. Y. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.03.036 · Cited by 223 · see citing works
Ensemble diversity measures and their application to thinning
Banfield, R. E., Hall, L., Bowyer, K. W., & Kegelmeyer, W. P. (2004) · Information Fusion · doi:10.1016/j.inffus.2004.04.005 · Cited by 221 · see citing works
Crone, S. F., Lessmann, S., & Stahlbock, R. (2005) · European Journal of Operational Research · doi:10.1016/j.ejor.2005.07.023 · Cited by 220 · see citing works
Self-citationSubagging for credit scoring models
Paleologo, G. A., Elisseeff, A., & Antonini, G. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.03.008 · Cited by 215 · see citing works
Using data mining to improve assessment of credit worthiness via credit scoring models
Wah, Y. B., Ong, S. H., & Husain, N. H. M. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.04.147 · Cited by 214 · see citing works
Multiple classifier architectures and their application to credit risk assessment
Finlay, S. (2010) · European Journal of Operational Research · doi:10.1016/j.ejor.2010.09.029 · Cited by 209 · see citing works
Consumer credit risk: Individual probability estimates using machine learning
Kruppa, J., Schwarz, A., Arminger, G., & Ziegler, A. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.03.019 · Cited by 201 · see citing works
Yu, L., Wang, S., & Lai, K. K. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2007.11.025 · Cited by 198 · see citing works
A probabilistic model of classifier competence for dynamic ensemble selection
Woloszynski, T., & Kurzynski, M. W. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.03.020 · Cited by 186 · see citing works
Bayesian kernel based classification for financial distress detection
Gestel, T. V., Baesens, B., Suykens, J. A. K., den Poel, D. V., Baestaens, D.-E., & Willekens, M. (2005) · European Journal of Operational Research · doi:10.1016/j.ejor.2004.11.009 · Cited by 185 · see citing works
Self-citationPruning in ordered bagging ensembles
Martínez-Muñoz, G., & Suárez, A. (2006) · International Conference on Machine Learning · doi:10.1145/1143844.1143921 · Cited by 183 · see citing works
Benchmarking regression algorithms for loss given default modeling
Loterman, G., Brown, I., Martens, D., Mues, C., & Baesens, B. (2011) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2011.01.006 · Cited by 180 · see citing works
Self-citationVerbraken, T., Bravo, C., Weber, R., & Baesens, B. (2014) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.04.001 · Cited by 179 · see citing works
Self-citationCombining cluster analysis with classifier ensembles to predict financial distress
Tsai, C. (2011) · Information Fusion · doi:10.1016/j.inffus.2011.12.001 · Cited by 176 · see citing works
Neural nets versus conventional techniques in credit scoring in Egyptian banking
Abdou, H. A., Pointon, J., & El‐Masry, A. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.08.030 · Cited by 169 · see citing works
Multiple classifier application to credit risk assessment
Twala, B. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.10.018 · Cited by 166 · see citing works
Vertical bagging decision trees model for credit scoring
Zhang, D., Zhou, X., Leung, S. C. H., & Zheng, J. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.04.054 · Cited by 163 · see citing works
Loss given default models incorporating macroeconomic variables for credit cards
Bellotti, A. G., & Crook, J. N. (2011) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2010.08.005 · Cited by 163 · see citing works
A data driven ensemble classifier for credit scoring analysis
Hsieh, N., & Hung, L. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.05.059 · Cited by 162 · see citing works
Huang, Y., Hung, C.-M., & Jiau, H. C. (2005) · Nonlinear Analysis Real World Applications · doi:10.1016/j.nonrwa.2005.04.006 · Cited by 162 · see citing works
Least squares support vector machines ensemble models for credit scoring
Zhou, L., Lai, K. K., & Yu, L. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.05.024 · Cited by 159 · see citing works
Abellán, J., & Mantas, C. J. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.12.003 · Cited by 150 · see citing works
On the suitability of resampling techniques for the class imbalance problem in credit scoring
Marqués, A. I., García, V., & Sánchez, J. S. (2012) · Journal of the Operational Research Society · doi:10.1057/jors.2012.120 · Cited by 149 · see citing works
Exploring the behaviour of base classifiers in credit scoring ensembles
Marqués, A. I., García, V., & Sánchez, J. S. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.02.092 · Cited by 145 · see citing works
Getting the Most Out of Ensemble Selection
Caruana, R., Munson, A., & Niculescu-Mizil, A. (2006) · Proceedings · doi:10.1109/icdm.2006.76 · Cited by 145 · see citing works
A survey of the issues in consumer credit modelling research
Thomas, L. C., Oliver, R. W., & Hand, D. J. (2005) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2602018 · Cited by 142 · see citing works
Self-citationIncorporating domain knowledge into data mining classifiers: An application in indirect lending
Sinha, A. P., & Zhao, H. (2008) · Decision Support Systems · doi:10.1016/j.dss.2008.06.013 · Cited by 141 · see citing works
Support vector machine based multiagent ensemble learning for credit risk evaluation
Yu, L., Yue, W., Wang, S., & Lai, K. K. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.06.083 · Cited by 140 · see citing works
The evaluation of consumer loans using support vector machines
Li, S.-L., Shiue, W., & HUANG, M. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.07.041 · Cited by 136 · see citing works
Tsoumakas, G., Partalas, I., & Vlahavas, I. P. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-03999-7_1 · Cited by 132 · see citing works
Sample selection bias in credit scoring models
Banasik, J., Crook, J. N., & Thomas, L. C. (2003) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2601578 · Cited by 131 · see citing works
Self-citationMixture cure models in credit scoring: If and when borrowers default
Tong, E. N. C., Mues, C., & Thomas, L. C. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.10.007 · Cited by 130 · see citing works
Self-citationA measure of competence based on random classification for dynamic ensemble selection
Woloszynski, T., Kurzynski, M. W., Podsiadło, P., & Stachowiak, G. (2011) · Information Fusion · doi:10.1016/j.inffus.2011.03.007 · Cited by 129 · see citing works
Consumer credit scoring models with limited data
Šušteršič, M., Mramor, D., & Zupan, J. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.06.016 · Cited by 129 · see citing works
Mining the customer credit using hybrid support vector machine technique
Chen, W., Ma, C., & Ma, L. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.054 · Cited by 123 · see citing works
Genetic programming for credit scoring: The case of Egyptian public sector banks
Abdou, H. A. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.01.076 · Cited by 120 · see citing works
Two-level classifier ensembles for credit risk assessment
Marqués, A. I., García, V., & Sánchez, J. S. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.03.033 · Cited by 119 · see citing works
Good practice in retail credit scorecard assessment
Hand, D. J. (2005) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2601932 · Cited by 118 · see citing works
Data mining feature selection for credit scoring models
Liu, Y., & Schümann, M. (2005) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2601976 · Cited by 116 · see citing works
Design of effective multiple classifier systems by clustering of classifiers
Giacinto, G., Roli, F., & Fumera, G. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2000.906039 · Cited by 112 · see citing works
Support vector regression for loss given default modelling
Xiao, Y., Crook, J. N., & Andreeva, G. (2014) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.06.043 · Cited by 107 · see citing works
Hens, A. B., & Tiwari, M. K. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.12.057 · Cited by 105 · see citing works
An ensemble uncertainty aware measure for directed hill climbing ensemble pruning
Partalas, I., Tsoumakas, G., & Vlahavas, I. P. (2010) · Machine Learning · doi:10.1007/s10994-010-5172-0 · Cited by 104 · see citing works
Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms
Frank Hoffmann, B., Baesens, B., Mues, C., Gestel, T. V., & Vanthienen, J. (2006) · European Journal of Operational Research · doi:10.1016/j.ejor.2005.09.044 · Cited by 104 · see citing works
Self-citationMunicipal credit rating modelling by neural networks
Hájek, P. (2010) · Decision Support Systems · doi:10.1016/j.dss.2010.11.033 · Cited by 100 · see citing works
Hand, D. J., & Anagnostopoulos, C. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.12.004 · Cited by 99 · see citing works
Credit scoring algorithm based on link analysis ranking with support vector machine
Xu, X., Zhou, C., & Wang, Z. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.01.024 · Cited by 99 · see citing works
Yu, L., Yao, X., Wang, S., & Lai, K. K. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.06.023 · Cited by 98 · see citing works
Neighborhood rough set and SVM based hybrid credit scoring classifier
Ping, Y., & Yongheng, L. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.02.179 · Cited by 98 · see citing works
The consumer loan default predicting model – An application of DEA–DA and neural network
Tsai, M.-C., Lin, S.-P., Cheng, C., & Lin, Y.-P. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.03.009 · Cited by 94 · see citing works
Reject inference, augmentation, and sample selection
Banasik, J., & Crook, J. N. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.06.072 · Cited by 84 · see citing works
Harris, T. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.01.044 · Cited by 83 · see citing works
Pruning an ensemble of classifiers via reinforcement learning
Partalas, I., Tsoumakas, G., & Vlahavas, I. P. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.06.007 · Cited by 81 · see citing works
Adaptive credit scoring with kernel learning methods
Yang, Y. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.10.066 · Cited by 75 · see citing works
Credit scoring for profitability objectives
Finlay, S. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.05.025 · Cited by 74 · see citing works
Cost-sensitive learning and decision making revisited
Viaene, S., & Dedene, G. (2004) · European Journal of Operational Research · doi:10.1016/j.ejor.2004.03.031 · Cited by 70 · see citing works
Credit rating prediction using Ant Colony Optimization
Martens, D., Gestel, T. V., Backer, M. D., Haesen, R., Vanthienen, J., & Baesens, B. (2009) · Journal of the Operational Research Society · doi:10.1057/jors.2008.164 · Cited by 66 · see citing works
Self-citationWinning the KDD Cup Orange Challenge with ensemble selection
Niculescu-Mizil, A., Perlich, C., Świrszcz, G., Sindhwani, V., Liu, Y., Melville, P., Wang, D., Xiao, J., Hu, J., Singh, M., et al. (2009) · Knowledge Discovery and Data Mining · Cited by 63 · see citing works
Modelling credit risk of portfolio of consumer loans
Malik, M., & Thomas, L. C. (2009) · Journal of the Operational Research Society · doi:10.1057/jors.2009.123 · Cited by 63 · see citing works
Self-citationConsumer finance: challenges for operational research
Thomas, L. C. (2009) · Journal of the Operational Research Society · doi:10.1057/jors.2009.104 · Cited by 62 · see citing works
Self-citationEvaluating and Tuning Predictive Data Mining Models Using Receiver Operating Characteristic Curves
Sinha, A. P., & May, J. H. (2004) · Journal of Management Information Systems · doi:10.1080/07421222.2004.11045815 · Cited by 60 · see citing works
Li, J., Wei, L., Li, G., & Xu, W. R. (2010) · Decision Support Systems · doi:10.1016/j.dss.2010.11.022 · Cited by 60 · see citing works
Granting and managing loans for micro-entrepreneurs: New developments and practical experiences
Bravo, C., Maldonado, S., & Weber, R. W. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2012.10.040 · Cited by 54 · see citing works
Filter- versus wrapper-based feature selection for credit scoring
Somol, P., Baesens, B., Pudil, P., & Vanthienen, J. (2005) · International Journal of Intelligent Systems · doi:10.1002/int.20103 · Cited by 54 · see citing works
Self-citationRandom effects logistic regression model for default prediction of technology credit guarantee fund
Sohn, S. Y., & Kim, H. (2006) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.10.006 · Cited by 54 · see citing works
European generic scoring models using survival analysis
Andreeva, G. (2005) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2602091 · Cited by 49 · see citing works
An Akaike information criterion for multiple event mixture cure models
Dirick, L., Claeskens, G., & Baesens, B. (2014) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.08.038 · Cited by 48 · see citing works
Self-citationQuantile regression for modelling distributions of profit and loss
Somers, M., & Whittaker, J. (2006) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.08.063 · Cited by 47 · see citing works
Clustering-based selective neural network ensemble
Fu, Q., Hu, S., & Shengying, Z. (2005) · 浙江大学学报：A卷英文版 · doi:10.1007/bf02839405 · Cited by 47 · see citing works
Greedy optimization classifiers ensemble based on diversity
Mao, S., Jiao, L., Xiong, L., & Gou, S. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.11.007 · Cited by 44 · see citing works
Downturn Loss Given Default: Mixture distribution estimation
Calabrese, R. (2014) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.01.043 · Cited by 43 · see citing works
Structural models in consumer credit
de Andrade, F. W. M., & Thomas, L. C. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.07.049 · Cited by 42 · see citing works
Self-citationFlórez‐López, R. (2009) · Journal of the Operational Research Society · doi:10.1057/jors.2009.66 · Cited by 41 · see citing works
A comparative study of data mining methods in consumer loans credit scoring management
Xiao, W., Qian, Z., & Qi, F. (2006) · Journal of Systems Science and Systems Engineering · doi:10.1007/s11518-006-5023-5 · Cited by 41 · see citing works
Evolutionary optimization of transition probability matrices for credit decision-making
Zhang, J., Avasarala, V., & Subbu, R. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.01.020 · Cited by 38 · see citing works
Random effects model for credit rating transitions
Kim, Y., & Sohn, S. Y. (2006) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.12.007 · Cited by 36 · see citing works
Adaptive consumer credit classification
Pavlidis, N. G., Tasoulis, D. K., Adams, N. M., & Hand, D. J. (2012) · Journal of the Operational Research Society · doi:10.1057/jors.2012.15 · Cited by 36 · see citing works
Hand, D. J., SOHN, S., & KIM, Y. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.04.032 · Cited by 36 · see citing works
Several multi-criteria programming methods for classification
Zhang, J., Shi, Y., & Zhang, P. (2007) · Computers & Operations Research · doi:10.1016/j.cor.2007.11.001 · Cited by 35 · see citing works
Gong, R., & Huang, S. H. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.12.011 · Cited by 31 · see citing works
Modelling the profitability of credit cards by Markov decision processes
So, M. C., & Thomas, L. C. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.01.023 · Cited by 30 · see citing works
Self-citationLessmann, S., Sung, M., JOHNSON, J. E. V., & Ma, T. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.10.032 · Cited by 29 · see citing works
Self-citationUpdating a credit-scoring model based on new attributes without realization of actual data
Ju, Y. H., & Sohn, S. Y. (2013) · European Journal of Operational Research · doi:10.1016/j.ejor.2013.02.030 · Cited by 28 · see citing works
Identifying future defaulters: A hierarchical Bayesian method
Liu, F., Hua, Z., & Lim, A. E. B. (2014) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.08.008 · Cited by 26 · see citing works
A New Goodness-of-Fit Test for Event Forecasting and Its Application to Credit Defaults
Blöchlinger, A., & Leippold, M. (2011) · Management Science · doi:10.1287/mnsc.1100.1283 · Cited by 21 · see citing works
Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis
Li, S., Tsang, I. W.-H., & Chaudhari, N. S. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.10.022 · Cited by 19 · see citing works
Heuristics for feature selection in mathematical programming discriminant analysis models
Falangis, K., & Glen, J. J. (2009) · Journal of the Operational Research Society · doi:10.1057/jors.2009.24 · Cited by 19 · see citing works
Credit risk model with contagious default dependencies affected by macro-economic condition
Takada, H., & Sumita, U. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.05.001 · Cited by 12 · see citing works
classifications of Credit Cardholder Behavior by Using Multiple Criteria Non-linear Programming
He, J., Shi, Y., & Xu, W. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30537-8_17 · Cited by 11 · see citing works
Handling selection bias when choosing actions in retail credit applications
Wu, I.-D., & Hand, D. J. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.10.063 · Cited by 11 · see citing works
Adapting a classification rule to local and global shift when only unlabelled data are available
Hofer, V. (2014) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.11.022 · Cited by 11 · see citing works
Comment: Classifier Technology and the Illusion of Progress—Credit Scoring
Gayler, R. W. (2006) · Statistical Science · doi:10.1214/088342306000000051 · Cited by 8 · see citing works
Induction over constrained strategic agents
Boylu, F., Aytuğ, H., & Kœhler, G. J. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.09.028 · Cited by 6 · see citing works
Flagged first, then by citations
4 of the 170 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [37]37. Hernández-Orallo, J., Flach, P. A., & Ramirez, C. F. (2011). Brier Curves: A New Cost-Based Visualisation of Classifier Performance. In L. Getoor & T. Scheffer (Eds.), Proc. of the 28th Intern. Conf. on Machine Learning (pp. 585-592). Bellevue, WA, USA: Omnipress.
- [64]64. Pundir, S., & Seshadri, R. (2012). A novel concept of partial lorenz curve and partial gini index. International Journal of Engineering ,Science and Innovative Technology, 1, 296-301.
Cited by
Sorted by citations
Zhang, W., Wu, C., Zhong, H., Li, Y., & Wang, L. (2020) · Geoscience Frontiers · doi:10.1016/j.gsf.2020.03.007 · Cited by 913 · see citing works
A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring
Xia, Y., Liu, C., Li, Y., & Liu, N. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.017 · Cited by 786 · see citing works
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions
Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173951 · Cited by 691 · see citing works
“[43, 38, 43, 55, 72]), we settled on the following contexts:”
Machine learning and natural language processing in mental health: systematic review
Le Glaz, A., Haralambous, Y., Kim-Dufor, D.-H., Lenca, P., Billot, R., Ryan, T. C., Marsh, J., DeVylder, J., Walter, M., Berrouiguet, S., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/15708 · Cited by 576 · see citing works
Caigny, A. D., Coussement, K., & Bock, K. W. D. (2018) · European Journal of Operational Research · doi:10.1016/j.ejor.2018.02.009 · Cited by 533 · see citing works
Machine Learning in Banking Risk Management: A Literature Review
Leo, M., Sharma, S. K., & Maddulety, K. (2019) · Risks · doi:10.3390/risks7010029 · Cited by 463 · see citing works
Dynamic classifier selection: Recent advances and perspectives
Cruz, R. M. O., Sabourin, R., & Cavalcanti, G. D. C. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.09.010 · Cited by 448 · see citing works
An up-to-date comparison of state-of-the-art classification algorithms
Zhang, C., Liu, C., Zhang, X., & Almpanidis, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.003 · Cited by 440 · see citing works
Dumitrescu, E., Hué, S., Hurlin, C., & Tokpavi, S. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.06.053 · Cited by 448 · see citing works
Quantum Computing for Finance: State-of-the-Art and Future Prospects
Egger, D. J., Gambella, C., Marecek, J., McFaddin, S., Mevissen, M., Raymond, R., Simonetto, A., Woerner, S., & Yndurain, E. (2020) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2020.3030314 · Cited by 422 · see citing works
Kraus, M., Feuerriegel, S., & Oztekin, A. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.09.018 · Cited by 392 · see citing works
Sun, J., Lang, J., Fujita, H., & Li, H. (2017) · Information Sciences · doi:10.1016/j.ins.2017.10.017 · Cited by 385 · see citing works
Kou, G., Xu, Y., Peng, Y., Shen, F., Chen, Y., Chang, K., & Kou, S. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113429 · Cited by 381 · see citing works
Determinants of Default in P2P Lending
Serrano‐Cinca, C., Nieto, B. G., & López-Palacios, L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139427 · Cited by 334 · see citing works
Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning
Engelmann, J., & Lessmann, S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114582 · Cited by 318 · see citing works
Self-citationTransformational Issues of Big Data and Analytics in Networked Business
Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L. (2016) · MIS Quarterly · doi:10.25300/misq/2016/40:4.03 · Cited by 296 · see citing works
Self-citationThe use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending
Serrano‐Cinca, C., & Nieto, B. G. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.06.014 · Cited by 260 · see citing works
A novel ensemble method for credit scoring: Adaption of different imbalance ratios
He, H., Zhang, W., & Zhang, S. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.01.012 · Cited by 250 · see citing works
A comparative study on base classifiers in ensemble methods for credit scoring
Abellán, J., & Castellano, J. G. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.12.020 · Cited by 250 · see citing works
Zhang, W., Li, H. R., Wu, C., Li, Y., Liu, Z., & Liu, H. (2020) · Underground Space · doi:10.1016/j.undsp.2019.12.003 · Cited by 244 · see citing works
Deep learning for credit scoring: Do or don’t?
Gunnarsson, B. R., Broucke, S. V., Baesens, B., Óskarsdóttir, M., & Lemahieu, W. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.03.006 · Cited by 256 · see citing works
Self-citationShen, F., Zhao, X., Kou, G., & Alsaadi, F. E. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106852 · Cited by 234 · see citing works
Machine learning techniques for credit risk evaluation: a systematic literature review
Bhatore, S., Mohan, L., & Reddy, Y. R. (2020) · Journal of Banking and Financial Technology · doi:10.1007/s42786-020-00020-3 · Cited by 228 · see citing works
Machine learning in agricultural and applied economics
Storm, H., Baylis, K., & Heckelei, T. (2019) · European Review of Agricultural Economics · doi:10.1093/erae/jbz033 · Cited by 227 · see citing works
Óskarsdóttir, M., Bravo, C., Sarraute, C., Vanthienen, J., & Baesens, B. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.10.004 · Cited by 229 · see citing works
Self-citationA novel heterogeneous ensemble credit scoring model based on bstacking approach
Xia, Y., Liu, C., Da, B., & Xie, F. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.10.022 · Cited by 215 · see citing works
Arora, N., & Kaur, P. D. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105936 · Cited by 211 · see citing works
A systematic review of the research trends of machine learning in supply chain management
Ni, D., Xiao, Z., & Lim, M. K. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-01050-0 · Cited by 206 · see citing works
Classifiers consensus system approach for credit scoring
Alaraj, M., & Abbod, M. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.04.013 · Cited by 202 · see citing works
Fairness in credit scoring: Assessment, implementation and profit implications
Kozodoi, N., Jacob, J., & Lessmann, S. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.06.023 · Cited by 198 · see citing works
Self-citationOperational Research and Artificial Intelligence Methods in Banking
Doumpos, M., Zopounidis, C., Gounopoulos, D., Platanakis, E., & Zhang, W. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2022.04.027 · Cited by 235 · see citing works
A deep learning approach for credit scoring using credit default swaps
Luo, C., Wu, D. D., & Wu, D. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.12.002 · Cited by 196 · see citing works
Bridging the divide in financial market forecasting: machine learners vs. financial economists
Hsu, M.-W., Lessmann, S., Sung, M.-C., Ma, T., & Johnson, J. E. V. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.05.033 · Cited by 195 · see citing works
Self-citationTransparency, auditability, and explainability of machine learning models in credit scoring
Bücker, M., Szepannek, G., Gosiewska, A., & Biecek, P. (2021) · Journal of the Operational Research Society · doi:10.1080/01605682.2021.1922098 · Cited by 225 · see citing works
Interpretable machine learning for imbalanced credit scoring datasets
Chen, Y., Calabrese, R., & Martin-Barragan, B. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.06.036 · Cited by 190 · see citing works
Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending
Xia, Y., Liu, C., & Liu, N. (2017) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2017.06.004 · Cited by 189 · see citing works
On the platform but will they buy? Predicting customers' purchase behavior using deep learning
Chaudhuri, N., Gupta, G., Vallurupalli, V., & Bose, I. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113622 · Cited by 184 · see citing works
Nguyen, D. K., Sermpinis, G., & Stasinakis, C. (2020) · European Financial Management · doi:10.1111/eufm.12365 · Cited by 224 · see citing works
Two-stage consumer credit risk modelling using heterogeneous ensemble learning
Papoušková, M., & Hájek, P. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.01.002 · Cited by 182 · see citing works
Predicting mortgage default using convolutional neural networks
Kvamme, H., Sellereite, N., Aas, K., & Sjursen, S. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.02.029 · Cited by 184 · see citing works
Jiang, C., Wang, Z., Wang, R., & Ding, Y. (2017) · Annals of Operations Research · doi:10.1007/s10479-017-2668-z · Cited by 179 · see citing works
Integration of unsupervised and supervised machine learning algorithms for credit risk assessment
Bao, W., Lianju, N., & Kong, Y. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.02.033 · Cited by 186 · see citing works
Pławiak, P., Abdar, M., & Acharya, U. R. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105740 · Cited by 173 · see citing works
Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending
Garzón, M. J. A., Arroyo, J., Caparrini, A., & Vargas, M. J. S. (2020) · IEEE Access · doi:10.1109/access.2020.2984412 · Cited by 181 · see citing works
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C. A., Casalicchio, G., Grosse-Wentrup, M., & Bischl, B. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-04083-2_4 · Cited by 211 · see citing works
Data engineering for fraud detection
Baesens, B., Höppner, S., & Verdonck, T. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113492 · Cited by 170 · see citing works
Self-citationA new hybrid ensemble credit scoring model based on classifiers consensus system approach
Alaraj, M., & Abbod, M. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.07.017 · Cited by 161 · see citing works
Instance spaces for machine learning classification
Muñoz, M. A., Villanova, L., Baatar, D., & Smith‐Miles, K. (2017) · Machine Learning · doi:10.1007/s10994-017-5629-5 · Cited by 153 · see citing works
A Comparative Performance Assessment of Ensemble Learning for Credit Scoring
Li, Y., & Chen, W. (2020) · Mathematics · doi:10.3390/math8101756 · Cited by 152 · see citing works
Extreme learning machines for credit scoring: An empirical evaluation
Bequé, A., & Lessmann, S. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.05.050 · Cited by 150 · see citing works
Self-citationBock, K. W. D., Coussement, K., Caigny, A. D., Słowiński, R., Baesens, B., Boute, R. N., Choi, T., Delen, D., Kraus, M., Lessmann, S., et al. (2023) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.09.026 · Cited by 156 · see citing works
Self-citationAn Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments
Munkhdalai, L., Munkhdalai, T., Namsrai, O.-E., Lee, J. Y., & Ryu, K. H. (2019) · Sustainability · doi:10.3390/su11030699 · Cited by 152 · see citing works
Data analytic approach for bankruptcy prediction
Son, H., Hyun, C., Phan, D., & Hwang, H. J. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.07.033 · Cited by 142 · see citing works
A XGBOOST RISK MODEL VIA FEATURE SELECTION AND BAYESIAN HYPER-PARAMETER OPTIMIZATION
Wang, Y., & Ni, X. S. (2019) · International Journal of Database Management Systems · doi:10.5121/ijdms.2019.11101 · Cited by 159 · see citing works
Enhancing credit scoring with alternative data
Djeundje, V. B., Crook, J. N., Calabrese, R., & Hamid, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113766 · Cited by 141 · see citing works
A multi-objective approach for profit-driven feature selection in credit scoring
Kozodoi, N., Lessmann, S., Papakonstantinou, K., Gatsoulis, Y., & Baesens, B. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.03.011 · Cited by 141 · see citing works
Self-citationThe value of vehicle telematics data in insurance risk selection processes
Baecke, P., & Bocca, L. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.04.009 · Cited by 152 · see citing works
AI in the Financial Sector: The Line between Innovation, Regulation and Ethical Responsibility
Ridzuan, N. N., Masri, M. H., Anshari, M., Fitriyani, N. L., & Syafrudin, M. (2024) · Information · doi:10.3390/info15080432 · Cited by 141 · see citing works
Zhang, W., He, H., & Zhang, S. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.12.020 · Cited by 139 · see citing works
Credit scoring based on tree-enhanced gradient boosting decision trees
Liu, W., Fan, H., & Xia, M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116034 · Cited by 145 · see citing works
Digitalisation and Big Data Mining in Banking
Hassani, H., Huang, X., & Silva, E. S. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2030018 · Cited by 132 · see citing works
Assessing credit risk of commercial customers using hybrid machine learning algorithms
Machado, M., & Karray, S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116889 · Cited by 130 · see citing works
Engineering safety in machine learning
Varshney, K. R. (2016) · Information Theory and Applications Workshop (ITA) · doi:10.1109/ita.2016.7888195 · Cited by 128 · see citing works
Zhang, H., Shi, Y., Yang, X., & Zhou, R. (2021) · Research in International Business and Finance · doi:10.1016/j.ribaf.2021.101482 · Cited by 128 · see citing works
Coussement, K., Phan, M., De Caigny, A., Benoit, D. F., & Raes, A. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113325 · Cited by 125 · see citing works
Golbayani, P., Florescu, I., & Chatterjee, R. (2020) · The North American Journal of Economics and Finance · doi:10.1016/j.najef.2020.101251 · Cited by 123 · see citing works
Defining analytics maturity indicators: A survey approach
Lismont, J., Vanthienen, J., Baesens, B., & Lemahieu, W. (2017) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.12.003 · Cited by 122 · see citing works
Self-citationKim, A., Yang, Y., Lessmann, S., Ma, T., Sung, M., & JOHNSON, J. E. V. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.11.007 · Cited by 120 · see citing works
Self-citationShen, F., Zhao, X., & Kou, G. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113366 · Cited by 120 · see citing works
Dynamic ensemble classification for credit scoring using soft probability
Feng, X., Xiao, Z., Zhong, B., Qiu, J. L., & Dong, Y. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.01.021 · Cited by 118 · see citing works
Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending
Wang, H., Kou, G., & Peng, Y. (2020) · Journal of the Operational Research Society · doi:10.1080/01605682.2019.1705193 · Cited by 116 · see citing works
Credit default prediction from user-generated text in peer-to-peer lending using deep learning
Kriebel, J., & Stitz, L. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.12.024 · Cited by 116 · see citing works
Fitzpatrick, T., & Mues, C. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.09.014 · Cited by 113 · see citing works
Automobile insurance classification ratemaking based on telematics driving data
Huang, Y., & Meng, S. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.113156 · Cited by 119 · see citing works
Malik, M., Abdallah, S., & Alaraj, M. (2016) · Annals of Operations Research · doi:10.1007/s10479-016-2393-z · Cited by 112 · see citing works
Ye, X., Dong, L.-A., & Ma, D. (2018) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2018.10.004 · Cited by 111 · see citing works
Predicting loan default in peer‐to‐peer lending using narrative data
Xia, Y., He, L., Li, Y., Liu, N., & Ding, Y. (2019) · Journal of Forecasting · doi:10.1002/for.2625 · Cited by 110 · see citing works
XGBoost Optimized by Adaptive Particle Swarm Optimization for Credit Scoring
Qin, C., Zhang, Y., Bao, F., Zhang, C., Лю, П., & Liu, P. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/6655510 · Cited by 110 · see citing works
Óskarsdóttir, M., Bravo, C., Verbeke, W., Sarraute, C., Baesens, B., & Vanthienen, J. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.05.028 · Cited by 108 · see citing works
Self-citationA novel tree-based dynamic heterogeneous ensemble method for credit scoring
Xia, Y., Zhao, J., He, L., Li, Y., & Niu, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113615 · Cited by 112 · see citing works
Ensemble learning models with a Bayesian optimization algorithm for mineral prospectivity mapping
Yin, J., & Li, N. (2022) · Ore Geology Reviews · doi:10.1016/j.oregeorev.2022.104916 · Cited by 109 · see citing works
Predictive Business Process Monitoring with Structured and Unstructured Data
Teinemaa, I., Dumas, M., Maggi, F. M., & Francescomarino, C. D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45348-4_23 · Cited by 108 · see citing works
Du, X., Hargreaves, C. J., Sheppard, J., Anda, F., Sayakkara, A., Le‐Khac, N., & Scanlon, M. (2020) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3407023.3407068 · Cited by 120 · see citing works
A new aspect on P2P online lending default prediction using meta-level phone usage data in China
Ma, L., Zhao, X., Zhou, Z., & Liu, Y. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.05.001 · Cited by 107 · see citing works
Mining Semantic Soft Factors for Credit Risk Evaluation in Peer-to-Peer Lending
Wang, Z., Jiang, C., Zhao, H., & Ding, Y. (2020) · Journal of Management Information Systems · doi:10.1080/07421222.2019.1705513 · Cited by 107 · see citing works
Wide and deep learning for peer-to-peer lending
Bastani, K., Asgari, E., & Namavari, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.042 · Cited by 105 · see citing works
Unsupervised quadratic surface support vector machine with application to credit risk assessment
Luo, J., Yan, X., & Tian, Y. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.08.010 · Cited by 104 · see citing works
Abedin, M. Z., Guotai, C., Hájek, P., & Zhang, T. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00614-4 · Cited by 100 · see citing works
Algorithmic discrimination in the credit domain: what do we know about it?
García, A. C. B., García, M., & C, R. R. B. (2023) · AI & Society · doi:10.1007/s00146-023-01676-3 · Cited by 127 · see citing works
Grabit: Gradient tree-boosted Tobit models for default prediction
Sigrist, F., & Hirnschall, C. (2019) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2019.03.004 · Cited by 98 · see citing works
An artificial intelligence system for predicting customer default in e-commerce
Vanneschi, L., Horn, D. M., Castelli, M., & Popovič, A. (2018) · Expert systems with applications · doi:10.1016/j.eswa.2018.03.025 · Cited by 78 · see citing works
Dahooie, J. H., Hajiagha, S. H. R., Farazmehr, S., Zavadskas, E. K., & Antuchevičienė, J. (2021) · Computers & Operations Research · doi:10.1016/j.cor.2021.105223 · Cited by 95 · see citing works
Dynamic prediction of financial distress using Malmquist DEA
Li, Z., Crook, J. N., & Andreeva, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.03.017 · Cited by 95 · see citing works
Chang, V. I. C., Hall, K., Xu, Q. A., Amao, F., Ganatra, M. A., & Benson, V. (2024) · Algorithms · doi:10.3390/a17060231 · Cited by 94 · see citing works
Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management
Milojević, N., & Redžepagić, S. (2021) · Journal of Central Banking Theory and Practice · doi:10.2478/jcbtp-2021-0023 · Cited by 93 · see citing works
Why you should stop predicting customer churn and start using uplift models
Devriendt, F., Berrevoets, J., & Verbeke, W. (2021) · Information Sciences · doi:10.1016/j.ins.2019.12.075 · Cited by 90 · see citing works
Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model
Carta, S., Fenu, G., Recupero, D. R., & Saia, R. (2019) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.02.007 · Cited by 89 · see citing works
Wang, Z., Jiang, C., Ding, Y., Lyu, X., & Liu, Y. (2017) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2017.12.006 · Cited by 89 · see citing works
Mushava, J., & Murray, M. P. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117233 · Cited by 90 · see citing works
- Authors
- Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- University of Southampton (GB)
- University of Nottingham Malaysia Campus (MY)
- Corresponding author
- Lessmann, Stefan
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2015-11
- Volume
- 247
- Issue
- 1
- Pages
- 124-136
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W2131816657
- Cited by
- 1,284
- References
- 170
- Citations by year
- 2026: 218 · 2025: 193 · 2024: 109 · 2023: 129 · 2022: 121 · 2021: 134 · 2020: 109 · 2019: 112 · 2018: 73 · 2017: 48 · 2016: 29 · 2015: 7 · 2014: 1 · 2012: 1
- FWCI
- 230.52
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- European Journal of Operational Research (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Lirias (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- credit scoring, machine learning classifiers, benchmarking, classifier comparison, credit risk assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
