Artificial Intelligence As A Research Tool In Entrepreneurial Finance: Implications For SME Financing And Sustainable Development In Asian Financial Ecosystems
DOI:
https://doi.org/10.69980/1c00jw16Keywords:
Artificial intelligence, Entrepreneurial finance, SME financing, Sustainable finance, Asian financial marketsAbstract
Artificial intelligence (AI) is dramatically changing financial research by changing the way knowledge is created, interpreted and utilized in decision-making situations. Drawing on a systematic qualitative synthesis of current academic literature, the research shows that artificial intelligence-based methodologies and specifically machine learning and deep learning methods consistently surpass traditional econometric methods in high-dimensional financial applications such as asset pricing, credit risk assessment, fraud detection and investment analytics. Beyond improvements in predictions, the use of AI transforms the epistemic foundations of the financial world in ways such as the ability to find complex nonlinear patterns or the incorporation of data-driven analytics at the level of institutional infrastructures. These advancements have important implications for entrepreneurial ecosystems, given the role that AI-based financial systems play in improving the credit assessment of SMEs, mitigating information asymmetries in the financial evaluation of start-ups, and improving fintech-enabled financial inclusion, particularly in fast digitalising Asian markets. AI-driven financial research will help in enhancing efficiency in allocating capital and underpinning sustainability-oriented investment practices to innovate-led growth and long-term economic resilience in emerging economies in Asia. Artificial intelligence is therefore a new research paradigm that shapes more inclusive, innovation-driven and sustainable financial systems.
References
1.Athey, S. (2018). The impact of machine learning on economics. In The economics of artificial intelligence: An agenda (pp. 507-547). University of Chicago Press.
2.Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11(1), 685-725.
3.Bartlett, R., Morse, A., Stanton, R., & Wallace, N. (2022). Consumer-lending discrimination in the FinTech era. Journal of Financial Economics, 143(1), 30-56.
4.Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.
5.Brynjolfsson, E., & McElheran, K. (2016). Data in action: Data-driven decision making in US manufacturing. University of Toronto-Rotman School of Management.
6.Cartea, Á., Jaimungal, S., & Penalva, J. (2015). Algorithmic and high-frequency trading. Cambridge University Press.
7.Al-Daoud, K. I., & Abu-AlSondos, I. A. (2025). Robust AI for financial fraud detection in the GCC: A hybrid framework for imbalance, drift, and adversarial threats. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 121.
8.Dixon, M., Klabjan, D., & Bang, J. H. (2017). Classification-based financial markets prediction using deep neural networks. Algorithmic Finance, 6(3-4), 67-77.
9.Fuster, A., Goldsmith‐Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably unequal? The effects of machine learning on credit markets. The Journal of Finance, 77(1), 5-47.
10.Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of management information systems, 35(1), 220-265.
11.Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
12.Harvey, C. R., Liu, Y., & Zhu, H. (2016). … and the cross-section of expected returns. The Review of financial studies, 29(1), 5-68.
13.Heaton, J. B., Polson, N. G., & Witte, J. H. (2017). Deep learning for finance: deep portfolios. Applied Stochastic Models in Business and Industry, 33(1), 3-12.
14.Lessmann, 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, 247(1), 124-136.
15.Loughran, T., & McDonald, B. (2016). Textual analysis in accounting and finance: A survey. Journal of accounting research, 54(4), 1187-1230.
16.Molnar, C. (2020). Interpretable machine learning. Lulu. com.
17.Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.
18.Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence, 1(5), 206-215.
19.Varian, H. (2018). Artificial intelligence, economics, and industrial organization. In The economics of artificial intelligence: An agenda (pp. 399-419). University of Chicago Press.
20.Vives, X. (2019). Digital disruption in banking. Annual Review of Financial Economics, 11(1), 243-272.
