Mapping Machine Learning Research Networks in Financial Risk Prediction
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References
- Altman, E. I. (1968). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy.
The Journal of Finance, 23 (4), 589–609.https://doi.org/10.1111/j.1540-6261.1968.tb00843.x">https://doi.org/10.1111/j.1540-6261.1968.tb00843.x - Anderson, R. (2007).
The credit scoring toolkit: Theory and practice for retail credit risk management and decision automation. Oxford University Press.https://doi.org/10.1093/oso/9780199226405.001.0001">https://doi.org/10.1093/oso/9780199226405.001.0001 - Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state‐of‐ the‐ art classification algorithms for credit scoring.
Journal of the Operational Research Society, 54 (6), 627–635.https://doi.org/10.1016/j.ejor.2015.05.030">https://doi.org/10.1016/j.ejor.2015.05.030 - Breiman, L. (2001). Random forests.
Machine Learning, 45 (1), 5–32.https://doi.org/10.1023/A:1010933404324">https://doi.org/10.1023/A:1010933404324 - Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). ACM.https://doi.org/10.1145/2939672.2939785">https://doi.org/10.1145/2939672.2939785 - Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines.
Journal of Business Research, 133 , 285–296.https://doi.org/10.1016/j.jbusres.2021.04.070">https://doi.org/10.1016/j.jbusres.2021.04.070 - Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning.
arXiv preprint arXiv:1702.08608.https://doi.org/10.48550/arXiv.1702.08608">https://doi.org/10.48550/arXiv.1702.08608 - Jagtiani, J., & Lemieux, C. (2019). The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform.
Financial Management, 48 (4), 1009–1029.https://doi.org/10.1111/fima.12295">https://doi.org/10.1111/fima.12295 - Li, J., Xu, C., Feng, B., & Zhao, H. (2023). Credit risk prediction model for listed companies based on CNN-LSTM and attention mechanism.
Electronics, 12 (7), 1643.https://doi.org/10.3390/electronics12071643">https://doi.org/10.3390/electronics12071643 - Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy.
Journal of Accounting Research, 18 (1), 109–131.https://doi.org/10.2307/2490395">https://doi.org/10.2307/2490395 - Onan, A. (2019). Consensus clustering-based undersampling approach to imbalanced learning.
Scientific Programming, 2019 , Article ID 5901087.https://doi.org/10.1155/2019/5901087">https://doi.org/10.1155/2019/5901087 - Ravi Kumar, P., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques—A review.
European Journal of Operational Research, 180 (1), 1–28.https://doi.org/10.1016/j.ejor.2006.08.043">https://doi.org/10.1016/j.ejor.2006.08.043 - West, D. (2000). Neural network credit scoring models.
Computers & Operations Research, 27 (11-12), 1131–1152.https://doi.org/10.1016/S0305-0548(99)00149-5">https://doi.org/10.1016/S0305-0548(99)00149-5 - Xia, Y., Liu, Y., Li, Y., & Liu, X. (2017). Boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring.
Expert Systems with Applications, 78 , 225–241.https://doi.org/10.1016/j.eswa.2017.02.017">https://doi.org/10.1016/j.eswa.2017.02.017
DOI: https://doi.org/10.2478/picbe-2025-0099 | Journal eISSN: 2558-9652
Language: English
Page range: 1248 - 1259
Published on: Jul 24, 2025
Published by: The Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
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© 2025 Cosmin Cojocaru, Sorin Ionescu, published by The Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.