7. Addison, T., Niño-Zarazúa, M., & Pirttilä, J. (2018). Fiscal policy, state building and economic development. Journal of International Development, 30(2), 161–172.

8. Akkoç, U. (2021). Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms. International Journal of Emerging Markets.

9. Alexopoulos, C., Lachana, Z., Androutsopoulou, A., Diamantopoulou, V., Charalabidis, Y., & Loutsaris, M. A. (2019). How machine learning is changing e-government. Proceedings of the 12th international conference on theory and practice of electronic governance.

10. Anastasopoulos, L. J., & Whitford, A. B. (2018). Machine learning for public administration research, with application to organizational reputation. Journal of Public Administration Research and Theory, 29(3), 491–510.

11. Bholat, D. (2015). Big data and central banks. Big Data & Society, 2(1), 205395171557946.

12. Boukherouaa, E. B., Shabsigh, G., AlAjmi, K., Deodoro, J., Farias, A., Iskender, E. S., Mirestean, A. T., & Ravikumar, R. (2021). Powering the digital economy: Opportunities and risks of artificial intelligence in finance. International Monetary Fund.

13. Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J.-F., Brañas-Garza, P., Butera, L., Douglas, K. M., Everett, J. A. C., Gigerenzer, G., Greenhow, C., Hashimoto, D. A., Holt-Lunstad, J., Jetten, J., Johnson, S., Kunz, W. H., Longoni, C., … Viale, R. (2024). The impact of generative artificial intelligence on socioeconomic inequalities and policy making. PNAS Nexus, 3(6), 191.

14. Chakraborty, C. & Joseph, A., (2017). Machine learning at central banks. Bank of England Working Paper No. 674, Available at SSRN: https://ssrn.com/abstract=3031796

15. Chandola, V., Banerjee, A. & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58,

16. Chapman, J. & Desai, A. (2022). Macroeconomic predictions using payments data and machine learning. arXiv (Cornell University).

17. Coe, C. K. (2008). Preventing local government fiscal crises: Emerging best practices. Public Administration Review, 68(4), 759–767.

18. Creswell, J. W. (2014). Research Design. 4th Ed. LA: SAGE Publication de Araujo, D. K. G., Doerr, S., Gambacorta, L., & Tissot, B. (2024). Artificial intelligence in central banking (No. 84). Bank for International Settlements.

19. Definition of Big Data - Gartner Information Technology Glossary, (2022). Gartner. https://www.gartner.com/en/information-technology/glossary/big-data#:%7E:text=Big%20data%20is%20high%2Dvolume,decision%20making%2C%20and%20process%20automation.

20. Desai, A. (2023). Machine learning for economics research: When what and how? arXiv (Cornell University).

21. Di Castri, S., Kulenkampff, A., Hohl, S., & Prenio, J. (2019). The suptech generations. Social Science Research Network.

22. Duijm, P., & van Lelyveld, I. (2024). Data science for central banks and supervisors: How to make it work, actually. Harvard Data Science Review, 7(1).

23. Elsayed, M., Abdelkader H., & Abdelwahab, A. (2020). Deep learning models for heterogeneous big data analytics. 15th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, pp. 1-5,

24. Foorthuis, R. (2021). On the nature and types of anomalies: A review of deviations in data. International Journal of Data Science and Analytics, 12(4), 297–331.

25. Gorodnichenko, Y., Pham, T., & Talavera, O. (2023). The voice of monetary policy. American Economic Review, 113(2), 548–584.

26. Guerra, P., Castelli, M., & Côrte-Real, N. (2022). Machine learning for liquidity risk modelling: A supervisory perspective. Economic Analysis and Policy, 74, 175–187.

27. Hajipour Sarduie, M., Afshar Kazemi, M., Alborzi, M., Azar, A., Kermanshah, A. (2020). P-V-L Deep: A big data analytics solution for now-casting in monetary policy. Journal of Information Technology Management, 12(4), 22-62.

28. Hakkarainen, P. (2020). Digitalizing banking supervision: an ongoing journey, not a final destination. Speech, The Supervision Innovators Conference, November 30, 2020.

29. Hinterlang, N., & Tänzer, A. (2021). Optimal monetary policy using reinforcement learning. Social Science Research Network.

30. Husted, L., Rogers, J., & Sun, B. (2020b). Monetary policy uncertainty. Journal of Monetary Economics, 115, 20–36.

31. Iversen, J., Laseen, S., Lundvall, H., & Soderstrom, U. (2016). Real-time forecasting for monetary policy analysis: The case of Sveriges Riksbank. SSRN Electronic Journal.

32. Jacob, S. (2024). Artificial intelligence and the future of evaluation: From augmented to automated evaluation. Digital Government: Research and Practice.

33. Kahyaoglu, H. (2021). The impact of artificial intelligence on central banking and monetary policies. Accounting, Finance, Sustainability, Governance & Fraud: Theory and Application, 83–98.

34. Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), pp. 2767–2787,

35. Khanzode K. C. A. & Sarode R. D., (2020). Advantages and disadvantages of artificial intelligence and machine Learning: A literature review. International Journal of Library & Information Science, 9(1), pp. 30-36.

36. Kohlscheen, E. (2022). What does machine learning say about the drivers of inflation? arXiv preprint arXiv: 2208.14653.

37. Loukis, E., Maragoudakis, M., & Kyriakou, N. (2019). Economic Crisis Policy Analytics Based on Artificial Intelligence. Lecture Notes in Computer Science, 262–275.

38. Vučinić, M., & Luburić, R. (2024). Artificial intelligence, fintech and challenges to central banks. Journal of Central Banking Theory and Practice, 13(3), pp. 5–42,

39. Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021). Machine learning for anomaly detection: A systematic review. IEEE Access, 9, 78658–78700.

40. Petropoulos, A., Siakoulis, V., Stavroulakis, E., & Klamargias, A. (2018). A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting. Proceedings of IFC – Bank Indonesia international workshop and seminar on “Big Data for Central Bank Policies / Building Pathways for Policy-Making with Big Data”, Bali.

41. Poloz, S. S. (2021). Technological progress and monetary policy: Managing the fourth industrial revolution. Journal of International Money and Finance, 114, p. 102373,

42. Rayner, B., & Bolhuis, M. (2020). Deus ex Machina? A framework for Macro Forecasting with Machine Learning. IMF Working Papers, 2020(045), 1.

43. Ruiz Estrada, M. A. (2019). The application of artificial intelligence in policy modeling. SSRN Electronic Journal

44. Toll, D., Lindgren, I., Melin, U., & Madsen, C. S. (2019). Artificial intelligence in Swedish policies: Values, benefits, considerations and risks. Lecture Notes in Computer Science, 301–310.

45. Umamaheswari, S., & Valarmathi, A. (2023). Role of artificial intelligence in the banking sector. Journal of Survey in Fisheries Sciences, 10(4S), 2841-2849.

46. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2018). Artificial intelligence and the public sector—applications and challenges. International Journal of Public Administration, 42(7), 596–615.

47. Yasir, M., Afzal, S., Latif, K., Chaudhary, G. M., Malik, N. Y., Shahzad, F., & Song, O. Y. (2020). An Efficient deep learning-based model to predict interest rate using twitter sentiment. Sustainability, 12(4), 1660.