[
Antal-Vaida, C. (2020). Business Analytics Applications for Consumer Credits, Database System Journal, 14-23.
]Search in Google Scholar
[
Bellotti, T., & Crook, J. (2009). Support vector machines for credit scoring and discovery of significant features. ScienceDirect - Expert Systems with Applications, 36, 3302-3308.10.1016/j.eswa.2008.01.005
]Search in Google Scholar
[
Doukidis, G., Mylonopoulos, N., & Pouloudi, N. (2004), Social and Economic Transformation in the Digital Era, IGI Global.10.4018/978-1-59140-158-2
]Search in Google Scholar
[
Dwight, M. (2013). A framework for Applying Analytics in Healthcare – What can be Learned from the Best Practices in Retail, Banking, Politics and Sports, Pearson Education Inc.
]Search in Google Scholar
[
Ha, V.-S., & Nguyen, H.-N. (2016). Credit scoring with a feature selection approach based deep learning. MATEC Web of Conference. 54. Cape Town, South Africa: EDP Sciences.
]Search in Google Scholar
[
Hamori, S., Kawai, M., Kume, T., Murakami, Y., & Watanabe, C. (2018). Ensemble Learning or Deep Learning? Application to Default Risk Analysis, Journal of Risk and Financial Management.10.3390/jrfm11010012
]Search in Google Scholar
[
Harris, T. (2013). Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions. Elsevier - Expert Systems with Applications, 40, 4404-4413.10.1016/j.eswa.2013.01.044
]Search in Google Scholar
[
Huang, C. L., Chen, M. C., & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines, ScienceDirect - Expert Systems with Applications, 33, 847-856.10.1016/j.eswa.2006.07.007
]Search in Google Scholar
[
Keramati, A., & Yousefi, N. (2011). A Proposed Classification of Data Mining Techniques in Credit Scoring, International Conference on Industrial Engineering and Operations Management, 416-424, Kuala Lumpur, Malaysia.
]Search in Google Scholar
[
Lessman, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research, European Journal of Operational Research.10.1016/j.ejor.2015.05.030
]Search in Google Scholar
[
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 2825-2830, Retrieved from Scikit learn: https://scikit-learn.org/stable/.
]Search in Google Scholar
[
Raghynathan, B., & Maiya, R. V. (2018). SMACing the Bank - How to Use Social Media, Mobility, Analytics, and Cloud Technologies to Transform the Business Processes of Banks and the banking Experience, CRC Press.
]Search in Google Scholar
[
Wang, H., Xu, Q., & Zhou, L. (2015). Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble, PloS Ones.10.1371/journal.pone.0117844433829225706988
]Search in Google Scholar
[
Yeh, I.-C. (2016). UCI Machine Learning Repository, Retrieved from https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients.
]Search in Google Scholar
[
Yu, L., Yang, Z., & Tang, L. (2016). A Novel Multistage Deep Belief Network Based Extreme Learning Machine Ensemble Learning Paradigms for Credit Risk Assessment, Flexible Services and Manufacturing Journal, 28.10.1007/s10696-015-9226-2
]Search in Google Scholar
[
Zhou, L., & Wang, H. (2012). Loan Default Prediction on Large Imbalanced Data Using Random Forests. TELKOMNIKA Indonesian Journal of Electrical Engineering, 10, 1519-1525.10.11591/telkomnika.v10i6.1323
]Search in Google Scholar
