References
- Abdelmoula, A. K. (2015). Bank credit risk analysis with k-nearest-neighbor classifier: Case of Tunisian banks. Accounting and Management Information Systems, 14(1), 79-106. https://doi.org/10.1109/OCIT59427.2023.10431007
- Abhiram, P., Artham, N., Reddy, N., & Kumari, K. V. (2023 ). Predicting the borrower’s genuineness in loan repayment through big data analytics. 2023 OITS International Conference on Information Technology (OCIT) (pp. 767-774). IEEE: Piscataway, NJ, USA. https://doi.org/10.1109/OCIT59427.2023.10431007
- Acito, F. (2023). k nearest neighbors. In F. Acito (Ed.), Predictive analytics with KNIME: Analytics for citizen data scientists (pp. 209-227). Cham, Switzerland: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-45630-5_10
- Adedapo, K. D. (2007). Analysis of default risk of agricultural loan by some selected commercial banks in Osogbo, Osun State, Nigeria. International Journal of Applied Agriculture and Apiculture Research, 4(1&2), 24-29.
- Alaloul, W. S., & Qureshi, A. H. (2020). Data processing using artificial neural networks. In D. Harkut (Ed.), Dynamic data assimilation: Beating the uncertainties (pp. 81–107). IntechOpen. https://doi.org/10.5772/intechopen.91935
- Ali, A., Hamraz, M., Gul, N., Khan, D. M., Aldahmani, S., & Khan, Z. (2023). A k nearest neighbour ensemble via extended neighbourhood rule and feature subsets. Pattern Recognition, 142(1), 109641. https://doi.org/10.1016/j.patcog.2023.109641
- Basha, S. A., Elgammal, M. M., & Abuzayed, B. M. (2021). Online peer-to-peer lending: A review of the literature. Electronic Commerce Research and Applications, 48, 101069. https://doi.org/10.1016/j.elerap.2021.101069
- Brownlee, J. (2016). XGBoost with Python: Gradient boosted trees with XGBoost and scikit-learn. S.l.: Machine Learning Mastery. https://machinelearningmastery.com/xgboost-with-python/
- Chen, D., Ye, J., & Ye, W. (2023). Interpretable selective learning in credit risk. Research in International Business and Finance, 65(C), 101940. https://doi.org/10.1016/j.ribaf.2023.101940
- Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). New York: Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
- Chen, Y. R., Leu, J. S., Huang, S. A., Wang, J. T., & Takada, J. I. (2021). Predicting default risk on peer-to-peer lending imbalanced datasets. IEEE Access, 9, 73103-73109. https://doi.org/10.1109/ACCESS.2021.3079701
- Chi Tin. (2023, 07 26). Ministry of Finance Makes a Breakthrough in Administrative Reform and Digital Transformation. Retrieved from Ministry of Finance of Vietnam: https://mof.gov.vn/webcenter/portal/ttncdtbh/pages_r/l/chi-tiettin?dDocName=MOFUCM278175
- Vietnam Governance. (2010, 6 16). Law No. 47/2010/QH12 by the National Assembly: LAW ON CREDIT INSTITUTIONS. Retrieved from Government Document System of Vietnam: https://vanban.chinhphu.vn/default.aspx?pageid=27160&docid=96074
- Dhruv, C., Paul, D., Kumar, M. H., & Reddy, M. S. (2023). Framework for bank loan repayment prediction and income prediction. 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) (pp. 833-840). Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/ICSCCC58608.2023.10176363
- Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020). A survey on ensemble learning. Frontiers of Computer Science, 14, 241-258. https://link.springer.com/article/10.1007/s11704-019-8208-z
- Emmanuel, I., Sun, Y., & Wang, Z. (2024). A machine learning-based credit risk prediction engine system using a stacked classifier and a filter-based feature selection method. Journal of Big Data, 11(1), 23. https://doi.org/10.1186/s40537-024-00882-0
- Fan, S. (2023). Design and implementation of a personal loan default prediction platform based on LightGBM model. 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA) (pp. 1232-1236). Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/ICPECA56706.2023.10076254
- Fang, J., & Ji, Z. (2024). Application of machine learning in loan default prediction. Mathematical Modeling and Algorithm Application, 2(2), 33-35. https://doi.org/10.54097/75k4fe13
- Fauzi, M. A., & Yuniarti, A. (2018). Ensemble method for indonesian twitter hate speech detection. Indonesian Journal of Electrical Engineering and Computer Science, 11(1), 294-299. http://doi.org/10.11591/ijeecs.v11.i1.pp294-299
- George, N. (2021, 2 1). All Lending Club loan data. Retrieved from Kaggle: https://www.kaggle.com/datasets/wordsforthewise/lending-club/data
- Gupta, A., Pant, V., Kumar, S., & Bansal, P. K. (2020). Bank Loan Prediction System using Machine Learning. 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART) (pp. 423-426). Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/SMART50582.2020.9336801
- Hakkal, S., & Lahcen, A. A. (2024). XGBoost to enhance learner performance prediction. Computers and Education: Artificial Intelligence, 7, 100254. https://doi.org/10.1016/j.caeai.2024.100254
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- Jayaram, E. S., Balachandar, G., & Kumar, K. (2024). Machine learning-based loan default prediction: Models, insights, and performance evaluation in peer-to-peer lending platforms. Educational Administration: Theory and Practice, 30(5), 12975-12989. http://dx.doi.org/10.53555/kuey.v30i5.5637
- Jin, Y., & Zhu, Y. (2015). A data-driven approach to predict default risk of loan for online peer-to-peer (P2P) lending. 2015 Fifth International Conference on Communication Systems and Network Technologies (pp. 609-613). Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/CSNT.2015.25
- Kabari, L. G., & Onwuka, U. C. (2019). Comparison of bagging and voting ensemble machine learning algorithm as a classifier. International Journals of Advanced Research in Computer Science and Software Engineering, 9(3), 19-23.
- Kalule, R., Abderrahmane, H. A., Alameri, W., & Sassi, M. (2023). Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs. Scientific Reports, 13(1), 9855. https://doi.org/10.1038/s41598-023-36096-2
- Ke, G., Meng, Q., Finely, T., Wang, T., Chen, W., Ma, W., . . . Liu, T. (2017, 12). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Retrieved from Microsoft Research1: https://www.microsoft.com/en-us/research/publication/lightgbm-a-highly-efficient-gradient-boosting-decision-tree/
- Kim, H., Cho, H., & Ryu, D. (2020). Corporate Default Predictions Using Machine Learning: Literature Review. Sustainability, 12(16), 6325. https://doi.org/10.3390/su12166325
- Koç, U., & Sevgili, T. (2020). Consumer loans’ first payment default detection: A predictive model. Turkish Journal of Electrical Engineering and Computer Sciences, 28(1), 167-181. https://doi.org/10.3906/elk-1809-190
- Kumari, S., Kumar, D., & Mittal, M. (2021). An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. International Journal of Cognitive Computing in Engineering, 2, 40-46. https://doi.org/10.1016/j.ijcce.2021.01.001
- Li, F., Zhang, L., Chen, B., Gao, D., Cheng, Y., Zhang, X., . . . Huang, Z. (2020). An optimal stacking ensemble for remaining useful life estimation of systems under multi-operating conditions. IEEE Access, 8, 31854-31868. https://doi.org/10.1109/ACCESS.2020.2973500
- Li, S., Ma, K., Niu, X., Wang, Y., Ji, K., Yu, Z., & Chen, Z. (2019). Stacking-based ensemble learning on low dimensional features for fake news detection. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2730-2735. https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00383
- Machado, M. R., Karray, S., & De Sousa, I. T. (2019). LightGBM: An effective decision tree gradient boosting method to predict customer loyalty in the finance industry. 2019 14th International Conference on Computer Science & Education (ICCSE) (pp. pp. 1111-1116). Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/ICCSE.2019.8845529
- Pandey, D., & Pandey, B. K. (2022). An efficient deep neural network with adaptive galactic swarm optimization for complex image text extraction. In (Eds), V. Yadav, A. K. Dubey, H. P. Singh, G. Dubey, & E. Suryani, Process mining techniques for pattern recognition (pp. 121-137). Boca Raton, FL: CRC Press. https://doi.org/10.1201/9781003169550-10
- Qi, X. (2023). Factors influence loan default–A credit risk analysis. International Conference on Economic Management and Green Development (pp. 849-862). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0523-8_79
- Rincy, T. N., & Gupta, R. (2020). Ensemble learning techniques and its efficiency in machine learning: A survey. 2020 2nd International Conference on Data, Engineering and Applications (IDEA) (pp. 1-6). Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/IDEA49133.2020.9170675
- Sain, K., & Kumar, P. C. (2022). An Overview of Artificial Neural Networks. In K. Sain, & P. C. Kumar, Meta-Attributes and Artificial Networking: A New Tool for Seismic Interpretation (pp. 73-93). Hoboken, New Jersey: John Wiley & Sons. https://doi.org/10.1002/9781119481874
- Satpute, S., Jayabalan, M., Kolivand, H., Assi, J., Aldhaibani, O. A., Liatsis, P., & Mahyoub, M. (2022). Loan default forecasting using StackNet. The International Conference on Data Science and Emerging Technologies (pp. 434-447). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-0741-0_31
- Schonlau, M. (2023). Logistic regression. In M. Schonlau, Applied statistical learning: With case studies in Stata (pp. 49-71). Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-031-33390-3_4
- Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006). Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. Australasian joint conference on artificial intelligence (pp. 1015-1021). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/11941439_114
- Thorat, M., Pandit, S., & Balote, S. (2022). Artificial neural network: A brief study. Asian Journal for Convergence in Technology (AJCT), 8(3), 12-16. https://doi.org/10.33130/AJCT.2022v08i03.003
- Uddin, N., Ahamed, M. K., Uddin, M. A., Islam, M. M., Talukder, M. A., & Aryal, S. (2023). An ensemble machine learning based bank loan approval predictions system with a smart application. International Journal of Cognitive Computing in Engineering, 4(6), 327-339. https://doi.org/10.1016/j.ijcce.2023.09.001
- Uddin, S., Haque, I., Lu, H., Moni, M. A., & Gide, E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports, 12(1), 6256. https://doi.org/10.1038/s41598-022-10358-x
- Wang, C., Han, D., Liu, Q., & Luo, S. (2018). A deep learning approach for credit scoring of peer-to-peer lending using attention mechanism LSTM. IEEE Access, 7, 2161-2168. https://doi.org/10.1109/ACCESS.2018.2887138
- Wang, W., Zuo, X., & Han, D. (2024). Predict credit risk with XGBoost. Applied and Computational Engineering, 74(1), 164-177. https://doi.org/10.54254/2755-2721/74/20240462
- Wolpert, D. H. (1992). Stacked generalization. Neural networks, 5(2), 241-259. https://doi.org/10.1016/S0893-6080(05)80023-1
- Xia, Y., He, L., Li, Y., Liu, N., & Ding, Y. (2020). Predicting loan default in peer-to-peer lending using narrative data. Journal of Forecasting, 39(2), 39(2), 260-280. https://doi.org/10.1002/for.2625
- Yadav, D., Sahoo, L., Mandal, S. K., Ravivarman, G., Vijayaraghavan, P., & Prasad, B. (2023). Using long short-term memory units for time series forecasting. 2023 2nd International Conference on Futuristic Technologies (INCOFT) (pp. 1-6). Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/INCOFT60753.2023.10425756
- Zhou, Y. (2023). Loan default prediction based on machine learning methods. Proceedings of the 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022). Zhengzhou, China: EAI. http://doi.org/10.4108/eai.2-12-2022.2328740