Deep learning for credit scoring: Do or don’t?

European Journal of Operational Research - Tập 295 - Trang 292-305 - 2021
Björn Rafn Gunnarsson1, Seppe vanden Broucke2,1, Bart Baesens1,3, María Óskarsdóttir4, Wilfried Lemahieu1
1Research Center for Information Systems Engineering (LIRIS), KU Leuven, Naamsestraat 69, Leuven 3000, Belgium
2Department of Business Informatics and Operations Management, UGent, Tweekerkenstraat 2, Ghent 9000, Belgium
3Department of Decision Analytics and Risk, University of Southampton, United Kingdom
4Department of Computer Science, Reykjavík University, Menntavegi 1, Reykjavík 101, Iceland

Tài liệu tham khảo

Adadi, 2018, Peeking inside the black-box: A survey on explainable artificial intelligence (XAI), IEEE Access, 6, 52138, 10.1109/ACCESS.2018.2870052 Addo, 2018, Credit risk analysis using machine and deep learning models, Risks, 6, 38, 10.3390/risks6020038 Akkoç, 2012, An empirical comparison of conventional techniques, neural networks and the three stage hybrid adaptive neuro fuzzy inference system (ANFIS) model for credit scoring analysis: The case of turkish credit card data, European Journal of Operational Research, 222, 168, 10.1016/j.ejor.2012.04.009 Baesens, 2014 Baesens, 2016 Baesens, 2003, Benchmarking state-of-the-art classification algorithms for credit scoring, Journal of the Operational Research Society, 54, 627, 10.1057/palgrave.jors.2601545 Benavoli, 2017, Time for a change: A tutorial for comparing multiple classifiers through Bayesian analysis, The Journal of Machine Learning Research, 18, 2653 Benavoli, 2014, A Bayesian Wilcoxon signed-rank test based on the Dirichlet process, 1026 Board of Governors of the Federal Reserve System (2019). Federal reserve statistical release. https://www.federalreserve.gov/releases/h8/current/default.htm. [Online; accessed 28-February-2019]. Bradley, 1997, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition, 30, 1145, 10.1016/S0031-3203(96)00142-2 Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 Chen, 2020, Predicting mortgage early delinquency with machine learning methods, European Journal of Operational Research Chen, 2016, XGBoost: A scalable tree boosting system, 785 Corani, 2017, Statistical comparison of classifiers through Bayesian hierarchical modelling, Machine Learning, 106, 1817, 10.1007/s10994-017-5641-9 Demšar, 2006, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, 7, 1 Deng, 2014, A tutorial survey of architectures, algorithms, and applications for deep learning, APSIPA Transactions on Signal and Information Processing, 3 Dua, D., & Graff, C. (2017). UCI machine learning repository. http://archive.ics.uci.edu/ml. Durand, 1941 García, 2010, Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power, Information Sciences, 180, 2044, 10.1016/j.ins.2009.12.010 Goodfellow, 2016 Greenland, 2016, Statistical tests, p values, confidence intervals, and power: A guide to misinterpretations, European Journal of Epidemiology, 31, 337, 10.1007/s10654-016-0149-3 Hamori, 2018, Ensemble learning or deep learning? Application to default risk analysis, Journal of Risk and Financial Management, 11, 12, 10.3390/jrfm11010012 Haykin, 1994, 2 He, 2018, A novel ensemble method for credit scoring: Adaption of different imbalance ratios, Expert Systems with Applications, 98, 105, 10.1016/j.eswa.2018.01.012 Hinton, 2012, A practical guide to training restricted Boltzmann machines, 599 Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647 Hollander, 2014, 751 Hosmer, 2013, 398 Hssina, 2014, A comparative study of decision tree ID3 and C4.5, International Journal of Advanced Computer Science and Applications, 4, 10.14569/SpecialIssue.2014.040203 Hua, 2015, Deep belief networks and deep learning, 1 Huang, 2006, Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem, Nonlinear Analysis: Real World Applications, 7, 720, 10.1016/j.nonrwa.2005.04.006 Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167. Jiang, 2019, A prediction-driven mixture cure model and its application in credit scoring, European Journal of Operational Research, 277, 20, 10.1016/j.ejor.2019.01.072 Kraus, 2020, Deep learning in business analytics and operations research: Models, applications and managerial implications, European Journal of Operational Research, 281, 628, 10.1016/j.ejor.2019.09.018 Kruschke, 2011 Kruschke, 2012, The time has come: Bayesian methods for data analysis in the organizational sciences, Organizational Research Methods, 15, 722, 10.1177/1094428112457829 Kruschke, 2018, The Bayesian new statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective, Psychonomic Bulletin & Review, 25, 178, 10.3758/s13423-016-1221-4 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Lesaffre, 2012 Lessmann, 2015, Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research, European Journal of Operational Research, 247, 124, 10.1016/j.ejor.2015.05.030 Lopes, 2015 Lundberg, 2017, A unified approach to interpreting model predictions, 4765 Luo, 2017, A deep learning approach for credit scoring using credit default swaps, Engineering Applications of Artificial Intelligence, 65, 465, 10.1016/j.engappai.2016.12.002 Maldonado, 2017, Integrated framework for profit-based feature selection and SVM classification in credit scoring, Decision Support Systems, 104, 113, 10.1016/j.dss.2017.10.007 Mancisidor, R. A., Kampffmeyer, M., Aas, K., & Jenssen, R. (2019). Deep generative models for reject inference in credit scoring. arXiv preprint arXiv:1904.11376. Marqués, 2012, Two-level classifier ensembles for credit risk assessment, Expert Systems with Applications, 39, 10916, 10.1016/j.eswa.2012.03.033 McCulloch, 1943, A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, 5, 115, 10.1007/BF02478259 Mohamed, 2009, Deep belief networks for phone recognition, 39 Mohamed, 2012, Acoustic modeling using deep belief networks, IEEE Transactions on Audio, Speech, and Language Processing, 20, 14, 10.1109/TASL.2011.2109382 Mohamed, 2011, Deep belief networks using discriminative features for phone recognition., 5060 Munkhdalai, 2019, Advanced neural network approach, its explanation with lime for credit scoring application, 407 Nuzzo, 2014, Scientific method: Statistical errors, Nature News, 506, 150, 10.1038/506150a Óskarsdóttir, 2019, The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics, Applied Soft Computing, 74, 26, 10.1016/j.asoc.2018.10.004 Papouskova, 2019, Two-stage consumer credit risk modelling using heterogeneous ensemble learning, Decision Support Systems, 118, 33, 10.1016/j.dss.2019.01.002 Ribeiro, 2016, “Why should I trust you?” Explaining the predictions of any classifier, 1135 Saberi, 2013, A granular computing-based approach to credit scoring modeling, Neurocomputing, 122, 100, 10.1016/j.neucom.2013.05.020 Schmidhuber, 2015, Deep learning in neural networks: An overview, Neural Networks, 61, 85, 10.1016/j.neunet.2014.09.003 Sharma, 2013, Classification through machine learning technique: C4.5 algorithm based on various entropies, International Journal of Computer Applications, 82, 10.5120/14249-2444 Spanoudes, P., & Nguyen, T. (2017). Deep learning in customer churn prediction: Unsupervised feature learning on abstract company independent feature vectors. arXiv preprint arXiv:1703.03869. Stevenson, M., Mues, C., & Bravo, C. (2020). The value of text for small business default prediction: A deep learning approach. arXiv preprint arXiv:2003.08964. Sun, 2018, Predicting credit card delinquencies: An application of deep neural networks, Intelligent Systems in Accounting, Finance and Management, 25, 174, 10.1002/isaf.1437 Svozil, 1997, Introduction to multi-layer feed-forward neural networks, Chemometrics and Intelligent Laboratory Systems, 39, 43, 10.1016/S0169-7439(97)00061-0 Thomas, 2002 Tieleman, 2012, Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude, COURSERA: Neural Networks for Machine Learning, 4, 26 Van Gestel, 2006, A process model to develop an internal rating system: Sovereign credit ratings, Decision Support Systems, 42, 1131, 10.1016/j.dss.2005.10.001 Van Gestel, 2005, Linear and nonlinear credit scoring by combining logistic regression and support vector machines, Journal of Credit Risk, 1, 10.21314/JCR.2005.025 Van-Sang, 2016, Credit scoring with a feature selection approach based deep learning, 54 Verbraken, 2014, Development and application of consumer credit scoring models using profit-based classification measures, European Journal of Operational Research, 238, 505, 10.1016/j.ejor.2014.04.001 Vinyals, 2011, Comparing multilayer perceptron to deep belief network tandem features for robust ASR, 4596 Wang, 2018, A deep learning approach for credit scoring of peer-to-peer lending using attention mechanism LSTM, IEEE Access, 7, 2161, 10.1109/ACCESS.2018.2887138 Wang, 2018, Personal credit risk assessment based on stacking ensemble model, 328 Wasserstein, 2016, The ASA’s statement on p-values: Context, process, and purpose, The American Statistician, 70, 129, 10.1080/00031305.2016.1154108 Xia, 2017, A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring, Expert Systems with Applications, 78, 225, 10.1016/j.eswa.2017.02.017 Xiao, 2006, A comparative study of data mining methods in consumer loans credit scoring management, Journal of Systems Science and Systems Engineering, 15, 419, 10.1007/s11518-006-5023-5 Yeh, 2009, The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients, Expert Systems with Applications, 36, 2473, 10.1016/j.eswa.2007.12.020 Yu, 2011, Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection, Expert Systems with Applications, 38, 15392, 10.1016/j.eswa.2011.06.023 Zhang, 2014, Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors, European Journal of Operational Research, 237, 335, 10.1016/j.ejor.2014.01.044 Zhou, 2010, Least squares support vector machines ensemble models for credit scoring, Expert Systems with Applications, 37, 127, 10.1016/j.eswa.2009.05.024 Zhu, 2018, A hybrid deep learning model for consumer credit scoring, 205