Nghiên cứu ứng dụng AI khả diễn trong FinTech: Tối ưu hóa đầu tư bền vững dựa trên tiêu chí ESG tại Việt Nam
DOI:
https://doi.org/10.24311/jabes/2025.36.2.07Keywords:
XAI, Fintech, Sustainable Investment, ESGAbstract
This study explores the role of eXplainable AI (XAI) in driving sustainable investment strategies within the financial technology (FinTech) sector in Vietnam. While AI models contribute to improving the efficiency of investment decision-making, transparency constraints are a significant barrier to the integration of Environmental, Social, and Governance (ESG) criteria. XAI is seen as a potential solution to address this issue by enhancing transparency, improving explainability, and ensuring accountability to strengthen investor confidence in the decision-making process. In the context of Vietnam's evolving landscape of AI and Big Data, along with the high growth potential of FinTech technology and applications: this study conducts a secondary data analysis of financial and ESG reports and applies advanced AI models combined with the SHAP mathematical framework to demonstrate how XAI can enhance resource allocation efficiency, risk management, and long-term sustainable development.
References
Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564-608.
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138-52160.
Anderson, H., Paddrik, M., & Wang, J. J. (2019). Bank networks and systemic risk: Evidence from the National Banking Acts. American Economic Review, 109(9), 3125-3161.
Alam, A., Banna, H., Alam, A. W., Bhuiyan, M. B. U., & Mokhtar, N. B. (2024). Climate change and geopolitical conflicts: The role of ESG readiness. Journal of Environmental Management, 353, 120284.
Albuquerque, R., Koskinen, Y., & Zhang, C. (2019). Corporate social responsibility and firm risk: Theory and empirical evidence. Management Science, 65(10), 4451-4469.
Angelov, P., & Soares, E. (2020). Towards explainable deep neural networks (xDNN). Neural Networks, 130, 185-194.
Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. (2021). Explainable artificial intelligence: An analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(5), e1424.
Bloomberg Intelligence. (2024). Global ESG assets predicted to hit $40 trillion by 2030, despite challenging environment, forecasts Bloomberg Intelligence. https://www.bloomberg.com/company/press/global-esg-assets-predicted-to-hit-40-trillion-by-2030-despite-challenging-environment-forecasts-bloomberg-intelligence/
Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2020). Explainable AI in fintech risk management. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00026
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1721-1730).
Černevičienė, J., & Kabašinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: A systematic literature review. Artificial Intelligence Review, 57(8), 216.
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250.
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.
Biecek, P., Chlebus, M., Gajda, J., Gosiewska, A., Kozak, A., Ogonowski, D.,… Wojewnik, P. (2021). Enabling machine learning algorithms for credit scoring -- Explainable artificial intelligence (XAI) methods for clear understanding complex predictive models. https://doi.org/10.48550/arXiv.2104.06735
Demajo, L. M., Vella, V., & Dingli, A. (2020). Explainable ai for interpretable credit scoring. https://doi.org/10.5121/csit.2020.101516
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. https://doi.org/10.48550/arXiv.1702.08608
Eccles, R. G., Ioannou, I., & Serafeim, G. (2014). The impact of corporate sustainability on organizational processes and performance. Management Science, 60(11), 2835-2857.
Elton, E. J., & Gruber, M. J. (1997). Modern portfolio theory, 1950 to date. Journal of Banking & Finance, 21(11-12), 1743-1759.
Fabozzi, F. J., Markowitz, H. M., & Gupta, F. (2008). Portfolio selection. In Handbook of Finance (Vol. 2, pp. 3-13). https://doi.org/10.1002/9780470404324.hof002001
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., . . . Rossi, F. (2018). AI4People – An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28, 689-707.
Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment, 5(4), 210-233.
Giese, G., Lee, L. E., Melas, D., Nagy, Z., & Nishikawa, L. (2019). Foundations of ESG investing: How ESG affects equity valuation, risk, and performance. Journal of Portfolio Management, 45(5), 69-83.
Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. The Quarterly Journal of Economics, 110(2), 353-377.
Holzinger, A. (2018). From machine learning to explainable AI. 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA), Košice, Slovakia, 2018, pp. 55-66.
Huỳnh Diệu Ngân. (2024). Hành trình ESG của Việt Nam: Thực trạng và giải pháp. https://kinhtevadubao.vn/hanh-trinh-esg-cua-viet-nam-thuc-trang-va-giai-phap-28995.html
Ioannou, I., & Serafeim, G. (2010). The impact of corporate social responsibility on investment recommendations. Academy of Management Proceedings, 2010(1). https://doi.org/10.5465/ambpp.2010.54493509
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning (1st ed.). Springer.
Khan, M., Serafeim, G., & Yoon, A. (2016). Corporate sustainability: First evidence on materiality. The Accounting Review, 91(6), 1697-1724.
Leung, C. K., Ko, J., & Chen, X. (2025). Economic crises and the erosion of sustainability: A global analysis of ESG performance in 100 countries (1990–2019). Innovation and Green Development, 4(2), 100226. https://doi.org/10.1016/j.igd.2025.100226
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 4768-4777).
Mention, A.-L. (2019). The future of fintech. Research-Technology Management, 62(4), 59-63. https://doi.org/10.1080/08956308.2019.1613123
Molnar, C. (2022). Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. Independently Published.
MSCI. (2024). MSCI ESG ratings methodology. Retrieved from https://www.msci.com/documents/1296102/34424357/MSCI+ESG+Ratings+Methodology.pdf
Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2005). Handbook on constructing composite indicators: methodology and user guide (OECD Statistics Working Papers No. 2005/03). Paris: OECD Publishing. https://doi.org/10.1787/533411815016
PwC. (2022). Báo cáo về Mức độ sẵn sàng thực hành ESG tại Việt Nam năm 2022. https://www.pwc.com/vn/vn/publications/vietnam-publications/esg-readiness-2022.html
Trần Ngọc Hùng. (2023). ESG Trong môi trường bất định COVID-19: Nghiên cứu thực nghiệm tại các doanh nghiệp Việt Nam. Tạp Chí Kinh tế & Phát triển, 311(2), 44-53.
Trương Đình Hải Thụy, & Nguyễn Thị Trần Lộc. (2025). Tăng cường ứng dụng trí tuệ nhân tạo (AI) trong phát triển kinh tế Việt Nam. https://kinhtevadubao.vn/tang-cuong-ung-dung-tri-tue-nhan-tao-ai-trong-phat-trien-kinh-te-viet-nam-31289.html
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