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

9. Akkoç, U. (2021). Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms. International Journal of Emerging Markets. [DOI:10.1108/ijoem-05-2020-0577]

10. 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. [DOI:10.1145/3326365.3326412.]

11. 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. [DOI:10.1093/jopart/muy060.]

12. Bholat, D. (2015). Big data and central banks. Big Data & Society, 2(1), 205395171557946. [DOI:10.1177/2053951715579469.]

13. 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.

14. 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. [DOI:10.1093/pnasnexus/pgae191.]

15. 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 or http://dx.doi.org/10.2139/ssrn.3031796.

16. Chandola, V., Banerjee, A. & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58, [DOI:10.1145/1541880.1541882.]

17. Chapman, J. & Desai, A. (2022). Macroeconomic predictions using payments data and machine learning. arXiv (Cornell University). [DOI:10.48550/arxiv.2209.00948]

18. Coe, C. K. (2008). Preventing local government fiscal crises: Emerging best practices. Public Administration Review, 68(4), 759–767. [DOI:10.1111/j.1540-6210.2008.00913.x]

19. Creswell, J. W. (2014). Research Design. 4th Ed. LA: SAGE Publication

20. de Araujo, D. K. G., Doerr, S., Gambacorta, L., & Tissot, B. (2024). Artificial intelligence in central banking (No. 84). Bank for International Settlements.

21. 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.

22. Desai, A. (2023). Machine learning for economics research: When what and how? arXiv (Cornell University). [DOI:10.48550/arxiv.2304.00086]

23. Di Castri, S., Kulenkampff, A., Hohl, S., & Prenio, J. (2019). The suptech generations. Social Science Research Network. [DOI:10.2139/ssrn.4232667]

24. 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).

25. 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, doi: 10.1109/ICCES51560.2020.9334569.

26. 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. [DOI:10.1007/s41060-021-00265-1]

27. Gorodnichenko, Y., Pham, T., & Talavera, O. (2023). The voice of monetary policy. American Economic Review, 113(2), 548–584. [DOI:10.1257/aer.20220129]

28. Guerra, P., Castelli, M., & Côrte-Real, N. (2022). Machine learning for liquidity risk modelling: A supervisory perspective. Economic Analysis and Policy, 74, 175–187. [DOI:10.1016/j.eap.2022.02.001]

29. 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. DOI: 10.22059/jitm.2020.293071.2429

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

31. Hinterlang, N., & Tänzer, A. (2021). Optimal monetary policy using reinforcement learning. Social Science Research Network. [DOI:10.2139/ssrn.4025682]

32. Husted, L., Rogers, J., & Sun, B. (2020b). Monetary policy uncertainty. Journal of Monetary Economics, 115, 20–36. [DOI:10.1016/j.jmoneco.2019.07.009]

33. Iversen, J., Laseen, S., Lundvall, H., & Soderstrom, U. (2016). Real-time forecasting for monetary policy analysis: The case of Sveriges Riksbank. SSRN Electronic Journal. [DOI:10.2139/ssrn.2780417]

34. Jacob, S. (2024). Artificial intelligence and the future of evaluation: From augmented to automated evaluation. Digital Government: Research and Practice. [DOI:10.1145/3696009. Accessed 8 Dec. 2024]

35. Kahyaoglu, H. (2021). The impact of artificial intelligence on central banking and monetary policies. Accounting, Finance, Sustainability, Governance & Fraud: Theory and Application, 83–98. [DOI:10.1007/978-981-33-6811-8-5.]

36. 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, [DOI:10.1016/j.jbankfin.2010.06.001.]

37. 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.

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

39. Loukis, E., Maragoudakis, M., & Kyriakou, N. (2019). Economic Crisis Policy Analytics Based on Artificial Intelligence. Lecture Notes in Computer Science, 262–275. [DOI:10.1007/978-3-030-27325-5-20]

40. 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, [DOI:10.2478/jcbtp-2024-0021.]

41. 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. [DOI:10.1109/access.2021.3083060]

42. 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.

43. Poloz, S. S. (2021). Technological progress and monetary policy: Managing the fourth industrial revolution. Journal of International Money and Finance, 114, p. 102373, [DOI:10.1016/j.jimonfin.2021.102373.]

44. Rayner, B., & Bolhuis, M. (2020). Deus ex Machina? A framework for Macro Forecasting with Machine Learning. IMF Working Papers, 2020(045), 1. [DOI:10.5089/9781513531724.00]

45. Ruiz Estrada, M. A. (2019). The application of artificial intelligence in policy modeling. SSRN Electronic Journal [DOI:10.2139/ssrn.3490762]

46. 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. [DOI:10.1007/978-3-030-27325-5-23]

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

48. 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. [DOI:10.1080/01900692.2018.1498103]

49. 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. [DOI:10.3390/su12041660]

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

57. Akkoç, U. (2021). Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms. International Journal of Emerging Markets. [DOI:10.1108/ijoem-05-2020-0577]

58. 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. [DOI:10.1145/3326365.3326412.]

59. 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. [DOI:10.1093/jopart/muy060.]

60. Bholat, D. (2015). Big data and central banks. Big Data & Society, 2(1), 205395171557946. [DOI:10.1177/2053951715579469.]

61. 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.

62. 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. [DOI:10.1093/pnasnexus/pgae191.]

63. 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 or http://dx.doi.org/10.2139/ssrn.3031796.

64. Chandola, V., Banerjee, A. & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58, [DOI:10.1145/1541880.1541882.]

65. Chapman, J. & Desai, A. (2022). Macroeconomic predictions using payments data and machine learning. arXiv (Cornell University). [DOI:10.48550/arxiv.2209.00948]

66. Coe, C. K. (2008). Preventing local government fiscal crises: Emerging best practices. Public Administration Review, 68(4), 759–767. [DOI:10.1111/j.1540-6210.2008.00913.x]

67. Creswell, J. W. (2014). Research Design. 4th Ed. LA: SAGE Publication

68. de Araujo, D. K. G., Doerr, S., Gambacorta, L., & Tissot, B. (2024). Artificial intelligence in central banking (No. 84). Bank for International Settlements.

69. 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.

70. Desai, A. (2023). Machine learning for economics research: When what and how? arXiv (Cornell University). [DOI:10.48550/arxiv.2304.00086]

71. Di Castri, S., Kulenkampff, A., Hohl, S., & Prenio, J. (2019). The suptech generations. Social Science Research Network. [DOI:10.2139/ssrn.4232667]

72. 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).

73. 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, doi: 10.1109/ICCES51560.2020.9334569.

74. 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. [DOI:10.1007/s41060-021-00265-1]

75. Gorodnichenko, Y., Pham, T., & Talavera, O. (2023). The voice of monetary policy. American Economic Review, 113(2), 548–584. [DOI:10.1257/aer.20220129]

76. Guerra, P., Castelli, M., & Côrte-Real, N. (2022). Machine learning for liquidity risk modelling: A supervisory perspective. Economic Analysis and Policy, 74, 175–187. [DOI:10.1016/j.eap.2022.02.001]

77. 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. DOI: 10.22059/jitm.2020.293071.2429

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

79. Hinterlang, N., & Tänzer, A. (2021). Optimal monetary policy using reinforcement learning. Social Science Research Network. [DOI:10.2139/ssrn.4025682]

80. Husted, L., Rogers, J., & Sun, B. (2020b). Monetary policy uncertainty. Journal of Monetary Economics, 115, 20–36. [DOI:10.1016/j.jmoneco.2019.07.009]

81. Iversen, J., Laseen, S., Lundvall, H., & Soderstrom, U. (2016). Real-time forecasting for monetary policy analysis: The case of Sveriges Riksbank. SSRN Electronic Journal. [DOI:10.2139/ssrn.2780417]

82. Jacob, S. (2024). Artificial intelligence and the future of evaluation: From augmented to automated evaluation. Digital Government: Research and Practice. [DOI:10.1145/3696009. Accessed 8 Dec. 2024]

83. Kahyaoglu, H. (2021). The impact of artificial intelligence on central banking and monetary policies. Accounting, Finance, Sustainability, Governance & Fraud: Theory and Application, 83–98. [DOI:10.1007/978-981-33-6811-8-5.]

84. 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, [DOI:10.1016/j.jbankfin.2010.06.001.]

85. 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.

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

87. Loukis, E., Maragoudakis, M., & Kyriakou, N. (2019). Economic Crisis Policy Analytics Based on Artificial Intelligence. Lecture Notes in Computer Science, 262–275. [DOI:10.1007/978-3-030-27325-5-20]

88. 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, [DOI:10.2478/jcbtp-2024-0021.]

89. 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. [DOI:10.1109/access.2021.3083060]

90. 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.

91. Poloz, S. S. (2021). Technological progress and monetary policy: Managing the fourth industrial revolution. Journal of International Money and Finance, 114, p. 102373, [DOI:10.1016/j.jimonfin.2021.102373.]

92. Rayner, B., & Bolhuis, M. (2020). Deus ex Machina? A framework for Macro Forecasting with Machine Learning. IMF Working Papers, 2020(045), 1. [DOI:10.5089/9781513531724.00]

93. Ruiz Estrada, M. A. (2019). The application of artificial intelligence in policy modeling. SSRN Electronic Journal [DOI:10.2139/ssrn.3490762]

94. 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. [DOI:10.1007/978-3-030-27325-5-23]

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

96. 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. [DOI:10.1080/01900692.2018.1498103]

97. 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. [DOI:10.3390/su12041660]