The Optimization of Credit Scoring Model Using Stacking Ensemble Learning and Oversampling Techniques
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Abstract
Credit risk assessment plays an important role in efficient and safe banking decision-making. Many studies have been conducted to analyze credit scoring with a focus on achieving high accuracy. However, predicting credit scoring decisions also requires model construction that handles class imbalance and proper model implementation. This research aims to increase the accuracy of credit assessment by balancing data using Synthetic Minority Oversampling (SMOTE) and applying ensemble stacking learning techniques. The proposed model utilizes a base learner consisting of Random Forest, SVM, Extra-Tree Classifier, and XGboost as a meta-learner. Then to handle unbalanced classes using SMOTE. The research process was carried out in several stages, namely Data Collection, Preprocessing, Oversampling, Modeling, and Evaluation. The model was tested using the German Credit dataset by applying cross-validation. The evaluation results show that the stacking ensemble learning model developed has optimal performance, with an accuracy of 83.21%, precision of 79.29%, recall of 91.78%, and f1-score of 85.08%. This research shows that optimizing the stacking ensemble learning model with data balancing using SMOTE in credit scoring can improve performance in credit scoring.
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References
Y. Chen, R. Calabrese, and B. Martin-Barragan, “Interpretable machine learning for imbalanced credit scoring datasets,” Eur. J. Oper. Res., no. xxxx, 2023, doi: 10.1016/j.ejor.2023.06.036.
Z. Zhang, Y. Li, Y. Liu, and S. Liu, “A local binary social spider algorithm for feature selection in credit scoring model,” Appl. Soft Comput., vol. 144, p. 110549, 2023, doi: 10.1016/j.asoc.2023.110549.
M. A. Muslim et al., “New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning,” Intell. Syst. with Appl., vol. 18, no. December 2022, p. 200204, 2023, doi: 10.1016/j.iswa.2023.200204.
X. Ma, J. Sha, D. Wang, Y. Yu, Q. Yang, and X. Niu, “Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning,” Electron. Commer. Res. Appl., vol. 31, no. February, pp. 24– 39, 2018, doi: 10.1016/j.elerap.2018.08.002.
X. Fu, S. Zhang, J. Chen, T. Ouyang, and J. Wu, “A Sentiment-Aware Trading Volume Prediction Model for P2P Market Using LSTM,” IEEE Access, vol. 7, pp. 81934–81944, 2019, doi: 10.1109/ACCESS.2019.2923637.
M. Di Maggio and V. Yao, “Fintech Borrowers: Lax Screening or Cream-Skimming?,” Rev. Financ. Stud., vol. 34, no. 10, pp. 4565–4618, 2021, doi: 10.1093/rfs/hhaa142.
Y. Xia, Y. Li, L. He, Y. Xu, and Y. Meng, “Incorporating multilevel macroeconomic variables into credit scoring for online consumer lending,” Electron. Commer. Res. Appl., vol. 49, no. March, p. 101095, 2021, doi: 10.1016/j.elerap.2021.101095.
C. Bai, B. Shi, F. Liu, and J. Sarkis, “Banking credit worthiness: Evaluating the complex relationships,” Omega (United Kingdom), vol. 83, pp. 26–38, 2019, doi: 10.1016/j.omega.2018.02.001.
F. Shen, X. Zhao, G. Kou, and F. E. Alsaadi, “A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique,” Appl. Soft Comput., vol. 98, p. 106852, 2021, doi: 10.1016/j.asoc.2020.106852.
Y. Kang, N. Jia, R. Cui, and J. Deng, “A graph-based semi-supervised reject inference framework considering imbalanced data distribution for consumer credit scoring,” Appl. Soft Comput., vol. 105, p. 107259, 2021, doi: 10.1016/j.asoc.2021.107259.
R. A. Mancisidor, M. Kampffmeyer, K. Aas, and R. Jenssen, “Deep generative models for reject inference in credit scoring,” Knowledge-Based Syst., vol. 196, p. 105758, 2020, doi: 10.1016/j.knosys.2020.105758.
H. Qian, P. Ma, S. Gao, and Y. Song, “Soft reordering one-dimensional convolutional neural network for credit scoring,” Knowledge-Based Syst., vol. 266, p. 110414, 2023, doi: 10.1016/j.knosys.2023.110414.
Y. Wang, Y. Jia, Y. Zhong, J. Huang, and J. Xiao, “Balanced incremental deep reinforcement learning based on variational autoencoder data augmentation for customer credit scoring,” Eng. Appl. Artif. Intell., vol. 122, no. March, p. 106056, 2023, doi: 10.1016/j.engappai.2023.106056.
H. He, Z. Wang, H. Jain, C. Jiang, and S. Yang, “A privacy-preserving decentralized credit scoring method based on multi-party information,” Decis. Support Syst., vol. 166, no. November 2022, p. 113910, 2023, doi: 10.1016/j.dss.2022.113910.
Y. Wu, W. Huang, Y. Tian, Q. Zhu, and L. Yu, “An uncertainty-oriented cost-sensitive credit scoring framework with multi-objective feature selection,” Electron. Commer. Res. Appl., vol. 53, no. March, p. 101155, 2022, doi: 10.1016/j.elerap.2022.101155.
D. M. B. Silva, G. H. A. Pereira, and T. M. Magalhães, “A class of categorization methods for credit scoring models,” Eur. J. Oper. Res., vol. 296, no. 1, pp. 323–331, 2022, doi: 10.1016/j.ejor.2021.04.029.
D. Şen, C. Ç. Dönmez, and U. M. Yıldırım, “A Hybrid Bi-level Metaheuristic for Credit Scoring,” Inf. Syst. Front., vol. 22, no. 5, pp. 1009–1019, 2020, doi: 10.1007/s10796-020-10037-0.
V. Medina-Olivares, R. Calabrese, J. Crook, and F. Lindgren, “Joint models for longitudinal and discrete survival data in credit scoring,” Eur. J. Oper. Res., vol. 307, no. 3, pp. 1457–1473, 2023, doi: 10.1016/j.ejor.2022.10.022.
D. Tripathi, D. R. Edla, V. Kuppili, and A. Bablani, “Evolutionary Extreme Learning Machine with novel activation function for credit scoring,” Eng. Appl. Artif. Intell., vol. 96, no. February, p. 103980, 2020, doi: 10.1016/j.engappai.2020.103980.
X. Dastile, T. Celik, and M. Potsane, “Statistical and machine learning models in credit scoring: A systematic literature survey,” Appl. Soft Comput. J., vol. 91, p. 106263, 2020, doi: 10.1016/j.asoc.2020.106263.
Y. Li, T. Bellotti, and N. Adams, “Issues Using Logistic Regression With Class Imbalance, With a Case Study From Credit Risk Modelling,” Found. Data Sci., vol. 1, no. 4, pp. 389–417, 2019, doi: 10.3934/fods.2019016.
K. Zhang et al., “Label correlation guided borderline oversampling for imbalanced multi-label data learning,” Knowledge-Based Syst., vol. 279, p. 110938, 2023, doi: 10.1016/j.knosys.2023.110938.
X. Tao, X. Guo, Y. Zheng, X. Zhang, and Z. Chen, “Self-adaptive oversampling method based on the complexity of minority data in imbalanced datasets classification,” Knowledge-Based Syst., vol. 277, p. 110795, 2023, doi: 10.1016/j.knosys.2023.110795.
G. E-mail, K. W. De Bock, K. Coussement, S. Lessmann, and K. W. De Bock, “Version of Record: https://www.sciencedirect.com/science/article/pii/S0377221720300898,” pp. 1–39.
W. Yin, B. Kirkulak-Uludag, D. Zhu, and Z. Zhou, “Stacking ensemble method for personal credit risk assessment in Peer-to-Peer lending,” Appl. Soft Comput., vol. 142, p. 110302, 2023, doi: 10.1016/j.asoc.2023.110302.
P. Sulikowski and T. Zdziebko, “Churn factors identification from real-world data in the telecommunications industry: Case study,” Procedia Comput. Sci., vol. 192, pp. 4800–4809, 2021, doi: 10.1016/j.procs.2021.09.258.
E. Dumitrescu, S. Hué, C. Hurlin, and S. Tokpavi, “Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects,” Eur. J. Oper. Res., vol. 297, no. 3, pp. 1178–1192, 2022, doi: 10.1016/j.ejor.2021.06.053.
W. F. Abror and M. Aziz, “Journal of Information System Bankruptcy Prediction Using Genetic Algorithm-Support Vector Machine ( GA-SVM ) Feature Selection and Stacking,” vol. 1, no. 2, pp. 103–108, 2023.
J. Mushava and M. Murray, “A novel XGBoost extension for credit scoring class-imbalanced data combining a generalized extreme value link and a modified focal loss function,” Expert Syst. Appl., vol. 202, no. March, p. 117233, 2022, doi: 10.1016/j.eswa.2022.117233.
A. Fernández, S. García, F. Herrera, and N. V. Chawla, “SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary,” J. Artif. Intell. Res., vol. 61, pp. 863–905, 2018, doi: 10.1613/jair.1.11192.
J. Sun, J. Lang, H. Fujita, and H. Li, “Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates,” Inf. Sci. (Ny)., vol. 425, pp. 76–91, 2018, doi: 10.1016/j.ins.2017.10.017.
A. Imakura, M. Kihira, Y. Okada, and T. Sakurai, “Another use of SMOTE for interpretable data collaboration analysis,” Expert Syst. Appl., vol. 228, no. January, 2023, doi: 10.1016/j.eswa.2023.120385.
A. Hoarau, A. Martin, J. C. Dubois, and Y. Le Gall, “Evidential Random Forests,” Expert Syst. Appl., vol. 230, no. February, p. 120652, 2023, doi: 10.1016/j.eswa.2023.120652.
Y. Liu, M. Yang, Y. Wang, Y. Li, and T. Xiong, “Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China,” Int. Rev. Financ. Anal., vol. 79, no. November 2021, p. 101971, 2022, doi: 10.1016/j.irfa.2021.101971.
M. A. Ganaie, A. Kumari, A. Girard, J. Kasa-Vubu, and M. Tanveer, “Diagnosis of Alzheimer’s disease via Intuitionistic fuzzy least squares twin SVM,” Appl. Soft Comput., vol. 149, no. September, 2023, doi: 10.1016/j.asoc.2023.110899.
H. F. Chen et al., “Predicting residual stress of aluminum nitride thin-film by incorporating manifold learning and tree-based ensemble classifier,” Mater. Chem. Phys., vol. 295, no. 300, p. 127070, 2023, doi: 10.1016/j.matchemphys.2022.127070.
S. M. Malakouti, “Improving the prediction of wind speed and power production of SCADA system with ensemble method and 10-fold cross-validation,” Case Stud. Chem. Environ. Eng., vol. 8, no. April, p. 100351, 2023, doi: 10.1016/j.cscee.2023.100351.
D. Tripathi, D. R. Edla, V. Kuppili, A. Bablani, and R. Dharavath, “Credit Scoring Model based on Weighted Voting and Cluster based Feature Selection,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 22–31, 2018, doi: 10.1016/j.procs.2018.05.055.
S. Guo, H. He, and X. Huang, “A Multi-Stage Self-Adaptive Classifier Ensemble Model With Application in Credit Scoring,” IEEE Access, vol. 7, pp. 78549–78559, 2019, doi: 10.1109/ACCESS.2019.2922676.
W. Liu, H. Fan, and M. Xia, “Step-wise multi-grained augmented gradient boosting decision trees for credit scoring,” Eng. Appl. Artif. Intell., vol. 97, no. May 2020, p. 104036, 2021, doi: 10.1016/j.engappai.2020.104036.