ARCHIVES

1. An, Y. J., Choi, P. M. S., & Huang, S. H. (2021). Blockchain, cryptocurrency, and artificial intelligence in finance. In Fintech with artificial intelligence, big data, and blockchain(pp. 1-34). Springer.
2. Ashta, A., & Herrmann, H. (2021). Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance. Strategic Change,30(3), 211-222.
3. Bhatore, S., Mohan, L., & Reddy, Y. R. (2020). Machine learning techniques for credit risk evaluation: a systematic literature review. Journal of Banking and Financial Technology,4(1), 111-138.
4. Guo, J., & Qiu, X. (2020, November). A Novel Financial Risk Analysis and Early Warning Method based on Data Mining. In2020 International Conference on Robots & Intelligent System (ICRIS)(pp. 374-377). IEEE.
5. Fu, J., Mandolfo, M., & Noci, G. (2024, May). Integrating Behavioral Finance Factors with Temporal Convolutional Networks for Enhanced Cryptocurrency Return Predictions. In2024 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)(pp. 660-664). IEEE.
6. Chowdhury, D., &Kulkarni, P. (2023). Application of data analytics in risk management of fintech companies. 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA),
7. Leal, A. A. (2022). Algorithms, creditworthiness, and lending decisions. International Conference on Autonomous Systems and the Law,
8. Omoge, A. P., Gala, P., & Horky, A. (2022). Disruptive technology and AI in the banking industry of an emerging market. International Journal of Bank Marketing,40(6), 1217-1247.
9. Ozili, P. K. (2023). Big data and artificial intelligence for financial inclusion: benefits and issues. In Artificial intelligence, fintech, and financial inclusion(pp. 1-10). CRC Press.
10. Salampasis, D. (2025). Advances on Fintech-Based Lending Practices: Orchestrating the Dialogue on Transformative Innovation. In The Palgrave Handbook of Breakthrough Technologies in Contemporary Organisations(pp. 415-429). Springer.
11. Shen, F., Zhao, X., & Kou, G. (2020). Three-stage reject inference learning framework for credit scoring using unsupervised transfer learning and three-way decision theory. Decision Support Systems,137, 113366.
12. Kakadiya, R., Khan, T., Diwan, A., & Mahadeva, R. (2024, October). Transformer Models for Predicting Bank Loan Defaults a Next-Generation Risk Management. In2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications(ICCCMLA)(pp. 26-31). IEEE.
13. Addy W, Ajayi-Nifise A, Bello B, Tula S, Odeyemi O, Falaiye T. AI in credit scoring: a comprehensive review of models and predictive analytics. Journal of Financial Services Marketing. 2024;18:118-129. https://doi.org/10.30574/gjeta.2024.18.2.0029.
14. Fares OH, Butt I, Lee SHM. Utilization of artificial intelligence in the banking sector: A systematic literature review. Journal of Financial Services Marketing; c2022. p. 1-18
15. Narang A, Vashisht P, Bhaskar S. Artificial intelligence in banking and finance. International Journal of Innovative Research in Computer Science and Technology. 2024;12:130-134. https://doi.org/10.55524/ijircst.2024.12.2.23
16. Takale D. Enhancing financial sentiment analysis: a deep dive into natural language processing for market prediction industries. Journal of Computer Networks and Virtualization. 2024, 2. https://doi.org/10.48001/jocnv.2024.221-5.
17. Li Y, Yi J, Chen H, Peng D. Theory and application of artificial intelligence in financial industry. Data Science in Finance and Economics. 2021;1(2):96-116.
18. Nayak, S. (2025). Leveraging Artificial Intelligence and Machine Learning for Real-Time Loan Approval Processes in FinTech. The Es Economics and Entrepreneurship, 3(03), 415 –. Retrieved from https://esj.eastasouth-institute.com/index.php/esee/article/view/695