From Regulation to Automation: Mapping Algorithmic Governance and RegTech in Financial System through a B-SLR Study
DOI:
https://doi.org/10.63503/aisem.60Keywords:
Governance, Reactive, Complexity, Algorithmic Governance, RegTech, B-SLRAbstract
Traditional governance mechanisms, characterised by fragmentation, reactivity, and reliance on periodic oversight, fail to cope with the scale, speed and complexity of the modern-day financial system. In this context, technological innovations such as algorithmic governance and RegTech have emerged as critical paradigms. These systems facilitate automated compliance and enhanced fraud detection, and are equipped with advanced predictive risk assessment capabilities. Such facilitation strengthens financial stability and institutional trust. The present study aims to conduct a B-SLR to provide a holistic and structured understanding of the field. This addresses the fragmented, limited efforts to systematically consolidate and analyse research developments in the domain.
References
[1] Vuković, D. B., Dekpo-Adza, S., & Matović, S. (2025). AI integration in financial services: A systematic review of trends and regulatory challenges. Humanities and Social Sciences Communications, 12(1), 1–29. https://doi.org/10.1057/s41599-025-04501-6
[2] Purwar, P. (2025). The transformational impact of AI across financial services. Journal of Computer Science and Technology Studies, 7(3), 461–469. https://doi.org/10.32996/jcsts.2025.7.3.37
[3] Musiani, F. (2013). Governance by algorithms. Internet Policy Review, 2(3). https://doi.org/10.14763/2013.3.187
[4] Gomber, P., Koch, J. A., & Siering, M. (2017). Digital finance and FinTech: Current research and future research directions. Journal of Business Economics, 87(5), 537–580. https://doi.org/10.1007/s11573-017-0852-x
[5] Anagnostopoulos, I. (2018). Fintech and regtech: Impact on regulators and banks. Journal of Economics and Business, 100, 7–25. https://doi.org/10.1016/j.jeconbus.2018.07.003
[6] Burrell, J. (2016). How the machine 'thinks': Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512
[7] Fernández, J. V. (2023). Artificial intelligence in government: Risks and challenges of algorithmic governance in the administrative state. Indiana Journal of Global Legal Studies, 30(1), 65–92. https://doi.org/10.2979/indjglolegstu.30.1.0065
[8] Shin, D., Fotiadis, A., & Yu, H. (2019). Prospectus and limitations of algorithmic governance: An ecological evaluation of algorithmic trends. Digital Policy, Regulation and Governance, 21(4), 369–383. https://doi.org/10.1108/DPRG-11-2018-0063
[9] Danaher, J., Hogan, M. J., Noone, C., Kennedy, R., Behan, A., De Paor, A., et al. (2017). Algorithmic governance: Developing a research agenda through the power of collective intelligence. Big Data & Society, 4(2). https://doi.org/10.1177/2053951717726554
[10] Bahoo, S., Cucculelli, M., Goga, X., & Mondolo, J. (2024). Artificial intelligence in finance: A comprehensive review through bibliometric and content analysis. SN Business & Economics, 4(2), Article 23. https://doi.org/10.1007/s43546-024-00603-5
[11] Nwachukwu, P. S., Chima, O. K., & Okolo, C. H. (2025). The artificial intelligence governance framework for finance: A control-by-design approach to algorithmic decision-making in accounting. Finance and Accounting Research Journal, 7(8), 350–379. https://doi.org/10.51594/farj.v7i8.1682
[12] David, A., Favour, O. A. E., Akorede, V. A., & Oluwatosin, R. (2024). Implementing data governance in financial systems: Strategies for ensuring compliance and security in multi-source data integration projects. Computer Science, 5(10), 2263–2282. https://doi.org/10.51594/csitrj.v5i10.1631
[13] Campbell-Verduyn, M., Goguen, M., & Porter, T. (2017). Big data and algorithmic governance: The case of financial practices. New Political Economy, 22(2), 219–236. https://doi.org/10.1080/13563467.2016.1216533
[14] Pūraitė, A., Zuzevičiūtė, V., Bereikienė, D., Skrypko, T., & Shmorgun, L. (2020). Algorithmic governance in public sector: Is digitization a key to effective management. Independent Journal of Management & Production, 11(9), 2149–2170. https://doi.org/10.14807/ijmp.v11i9.1411
[15] Musella, F. (2025). Algorithmic governance. In Public administration in Italy: The science and the profession (pp. 203–218). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-84043-2_13
[16] Kango, U. (2025). Algorithmic governance. In Handbook of human-centered artificial intelligence (pp. 1–26). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-0267-4_50-1
[17] Fieberg, C., Hesse, M., Liedtke, G., & Zaremba, A. (2025). Predicting financial stability with TopicGPT: Insights from corporate and central bank communications. Journal of Banking & Finance, Article 107598. https://doi.org/10.1016/j.jbankfin.2025.107598
[18] Batool, A., Zowghi, D., & Bano, M. (2025). AI governance: A systematic literature review. AI and Ethics, 5(3), 3265–3279. https://doi.org/10.1007/s43681-024-00561-9
[19] Yang, D., Tang, K., & Yang, S. (2026). Algorithmic governance and corporate legal compliance: Mechanisms and evidence on how artificial intelligence improves the judicial environment. Finance Research Letters, Article 109733. https://doi.org/10.1016/j.frl.2025.109733
[20] Latzer, M., & Festic, N. (2019). A guideline for understanding and measuring algorithmic governance in everyday life. Internet Policy Review, 8(2). https://doi.org/10.14763/2019.2.1412
[21] Peeters, R., & Schuilenburg, M. (2023). Algorithmic governance: Technology, knowledge and power. In The SAGE handbook of digital society (pp. 439–457). SAGE Publications. https://doi.org/10.4135/9781529783223.n28
[22] Grassi, L., & Lanfranchi, D. (2022). RegTech in financial services: A systematic literature review. Journal of Financial Regulation and Compliance, 30(4), 1–18. https://doi.org/10.1108/JFRC-05-2021-0038
[23] Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523. https://doi.org/10.1111/rego.12158
[24] Zetzsche, D. A., Buckley, R. P., Barberis, J. N., & Arner, D. W. (2017). Regulating a revolution: From regulatory sandboxes to smart regulation. Fordham Journal of Corporate & Financial Law, 23(1), 31–103. https://doi.org/10.2139/ssrn.3018534
[25] Arner, D. W., Buckley, R. P., Barberis, J. N., & Zetzsche, D. A. (2017). FinTech and RegTech in a nutshell, and the future in a sandbox. CFA Institute Research Foundation Briefs, 3(4), 1–20. https://doi.org/10.2139/ssrn.3088303
[26] Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29. https://doi.org/10.1080/1369118X.2016.1154087
[27] Zupic, T., & Čater, A. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
[28] Linnenluecke, M. K., Marrone, M., & Singh, A. (2020). Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), 175–194. https://doi.org/10.1177/0312896219877678
[29] Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
[30] Marzi, G., Balzano, M., Caputo, A., & Pellegrini, M. M. (2025). Guidelines for bibliometric-systematic literature reviews: 10 steps to combine analysis, synthesis and theory development. International Journal of Management Reviews, 27(1), 81–103. https://doi.org/10.1111/ijmr.12381
[31] Diaz, A., Morales, J., & Sharma, R. K. (2025). Explainable AI and governance challenges in financial decision-making systems. Journal of Financial Regulation and Compliance, 33(2), 145–162. https://doi.org/10.1108/JFRC-06-2024-0089
[32] Joamets, A. (2025). Explainable artificial intelligence and algorithmic accountability in financial governance. AI and Society, 40(1), 85–101. https://doi.org/10.1007/s00146-023-01789-2
[33] Vági, E. (2025). The EU AI Act and the governance of high-risk financial AI systems. European Business Law Review, 36(1), 55–78. https://doi.org/10.54648/EULR2025004
[34] Jonnala, S., Mehta, P., & Iyer, R. (2025). Regulatory implications of the EU AI Act for financial governance and compliance. Technology in Society, 82, Article 102421. https://doi.org/10.1016/j.techsoc.2025.102421
[35] Yeasmin, S., & Alshemaimri, A. (2025). Algorithmic automation and regulatory compliance: Challenges in principles-based governance. Journal of Financial Regulation and Compliance, 33(1), 44–61. https://doi.org/10.1108/JFRC-04-2024-0062
[36] Rafiq, M., & Sohail, M. (2025). RegTech adoption and risk-based compliance frameworks in financial institutions. Technology in Society, 81, Article 102390. https://doi.org/10.1016/j.techsoc.2025.102390
[37] Bagherifam, A., Costa, L., & Mendes, P. (2025). Governance frameworks and AI-driven regulatory automation in financial systems. AI and Ethics, 5(2), 1881–1898. https://doi.org/10.1007/s43681-024-00572-6
[38] Krause, T., & Krause, J. (2025). Risk-based approaches and RegTech integration in AML/CFT governance. Journal of Risk Management in Financial Institutions, 18(2), 112–129. https://doi.org/10.69554/OEJH4536
[39] Sarabdeen, H. (2024). Data privacy governance and GDPR compliance in AI-enabled financial systems. Information & Computer Security, 32(3), 411–428. https://doi.org/10.1108/ICS-08-2023-0142
[40] Damaris, R., Evans, P., & Clarke, T. (2025). Information ethics and responsible AI governance in financial technologies. AI and Ethics, 5(4), 2401–2418. https://doi.org/10.1007/s43681-024-00583-3
[41] Chen, Y., Wang, L., & Zhao, H. (2025). Technology adoption frameworks and RegTech implementation in financial institutions. Journal of Financial Innovation, 11(2), 88–105. https://doi.org/10.1186/s40854-024-00632-4
[42] Billones, M., Santos, R., & Cruz, P. (2025). Socio-technical perspectives on FinTech and algorithmic governance adoption. Technology in Society, 82, Article 102455. https://doi.org/10.1016/j.techsoc.2025.102455
[43] Georgosouli, A. (2023). Institutional legitimacy and RegTech adoption in financial governance. Journal of Banking Regulation, 24(3), 211–226. https://doi.org/10.1057/s41261-022-00189-2
[44] Devereaux, J. (2025). Behavioural dimensions of algorithmic decision-making in financial systems. Behaviour & Information Technology, 44(1), 55–72. https://doi.org/10.1080/0144929X.2024.2334521
[45] Hayat, A., Rahman, M., & Lee, S. (2025). Behavioural finance and algorithmic governance: Decision-making in AI-driven financial environments. Finance Research Letters, 65, Article 105214. https://doi.org/10.1016/j.frl.2024.105214
[46] Kera, K. (2019). Distributed ledger technologies and algorithmic governance in financial systems. Journal of Financial Transformation, 49, 88–101. https://doi.org/10.2139/ssrn.3425671
[47] El, E., Hassan, M., & Abdullah, T. (2025). Machine learning applications in AML transaction monitoring systems. Journal of Financial Crime, 32(1), 88–105. https://doi.org/10.1108/JFC-03-2024-0071
[48] Krause, T., Krause, J., & Richter, P. (2025). AI-driven credit risk assessment and regulatory governance in banking. Journal of Risk Finance, 26(2), 140–158. https://doi.org/10.1108/JRF-07-2024-0182
[49] Remolina, A. (2025). Algorithmic credit scoring and governance challenges in digital finance. Banking and Finance Law Review, 40(1), 67–89. https://doi.org/10.2139/ssrn.4812345
[50] Solaimani, H., Dehghani, R., & Foster, P. (2025). Fraud detection and anomaly identification using AI-driven financial analytics. Expert Systems with Applications, 245, Article 122998. https://doi.org/10.1016/j.eswa.2024.122998
[51] Alqudah, M., Alshira'H, A., & Al-Okaily, S. (2025). Graph neural networks and fraud detection in financial systems. Decision Support Systems, 185, Article 114321. https://doi.org/10.1016/j.dss.2024.114321
[52] Kazbekova, A., Smirnov, I., & Petrov, Y. (2024). LSTM-based time-series forecasting in financial regulatory systems. Finance Research Letters, 61, Article 104782. https://doi.org/10.1016/j.frl.2023.104782
[53] Zhuravel, O., Ivanov, M., & Petrova, L. (2025). Systematic literature review of AI governance in financial systems. AI and Society, 40(2), 411–429. https://doi.org/10.1007/s00146-024-01892-3
[54] Bagherifam, A., Costa, L., & Mendes, P. (2025). Governance frameworks and AI-driven regulatory automation in financial systems. AI and Ethics, 5(2), 1881–1898. https://doi.org/10.1007/s43681-024-00572-6
[55] Tahvildaria, S. (2025). Bibliometric perspectives on RegTech and algorithmic governance research. Technology Analysis & Strategic Management, 37(1), 52–70. https://doi.org/10.1080/09537325.2024.2312847
[56] Fundira, P., Ndlovu, T., & Moyo, S. (2025). Mapping the intellectual structure of RegTech research using bibliometric techniques. Scientometrics, 130(3), 1891–1915. https://doi.org/10.1007/s11192-024-05201-4
[57] Sánchez-Camacho, J., Ortega, R., & Ruiz, D. (2025). Scopus and VOSviewer-based bibliometric analysis of financial technology governance. Information Development, 41(2), 233–250. https://doi.org/10.1177/02666669241234567
[58] Flórez, M. (2025). SupTech pilot implementation and governance evaluation: A comparative case study. Journal of Financial Regulation and Compliance, 33(3), 201–220. https://doi.org/10.1108/JFRC-08-2024-0134
[59] Gunawan, A., Lim, H., & Prakash, S. (2025). PLS-SEM analysis of RegTech adoption using TOE and UTAUT frameworks. Technology in Society, 81, Article 102376. https://doi.org/10.1016/j.techsoc.2025.102376
[60] Cheng, Y., Li, P., & Wang, R. (2025). Simulation-based regulatory forecasting and AI risk modelling in finance. Expert Systems with Applications, 244, Article 122744. https://doi.org/10.1016/j.eswa.2024.122744
[61] Gruetzemacher, J., & Whittlestone, A. (2025). Simulation and reinforcement learning approaches in AI governance research. AI and Ethics, 5(1), 155–172. https://doi.org/10.1007/s43681-024-00541-z
[62] Yeasmin, S., Alshemaimri, A., & Rahman, M. (2025). Large language models and NLP-driven compliance automation in financial regulation. AI and Ethics, 5(3), 2105–2124. https://doi.org/10.1007/s43681-024-00578-2
[63] Alboaneen, W., Alsharif, H., & Malik, T. (2025). Natural language processing applications in regulatory technology and automated compliance systems. Information Systems Frontiers, 27(2), 455–473. https://doi.org/10.1007/s10796-024-10521-4
[64] Lee, J., Kim, S., & Park, A. (2025). Generative AI and regulatory text analytics in financial governance. Expert Systems with Applications, 246, Article 123112. https://doi.org/10.1016/j.eswa.2024.123112
[65] Mahmud, M., Ahmed, R., & Chowdhury, S. (2025). Mixed-method approaches in algorithmic governance research: Bridging computational and institutional analysis. Technology in Society, 82, Article 102488. https://doi.org/10.1016/j.techsoc.2025.102488
[66] Coovadia, N., Singh, P., & Jacobs, L. (2025). Institutional governance and AI regulation: A mixed-method investigation. Government Information Quarterly, 42(1), Article 101912. https://doi.org/10.1016/j.giq.2024.101912
[67] Ridzuan, N. (2024). Methodological challenges in AI governance and RegTech research. Journal of Information, Communication and Ethics in Society, 22(4), 377–392. https://doi.org/10.1108/JICES-05-2024-0062
[68] Jin, Y., Park, H., & Kim, S. (2024). Blockchain-based KYC and decentralized compliance systems in financial governance. Journal of Financial Innovation, 10(4), 301–319. https://doi.org/10.1186/s40854-024-00598-3
[69] Loang, T. (2025). Automation of regulatory compliance through RegTech systems in banking institutions. Journal of Banking Regulation, 26(1), 77–94. https://doi.org/10.1057/s41261-024-00221-7
[70] Singh, A., Verma, R., & Chatterjee, P. (2022). Algorithmic sanctions screening and politically exposed person detection in financial compliance. Journal of Financial Crime, 29(3), 844–861. https://doi.org/10.1108/JFC-08-2021-0178
[71] Bakhos, E., Alami, M., & Peters, J. (2023). Counter-terrorism financing compliance and AI-driven sanctions screening systems. Journal of Money Laundering Control, 26(4), 955–973. https://doi.org/10.1108/JMLC-11-2022-0142
[72] Johnson, L. (2021). Beneficial ownership opacity and sanctions enforcement challenges in global finance. International Review of Law and Economics, 68, Article 106021. https://doi.org/10.1016/j.irle.2021.106021
[73] Lyons, C., Howard, P., & Ellis, S. (2025). AI auditing frameworks and algorithmic accountability in financial systems. AI and Ethics, 5(2), 1651–1669. https://doi.org/10.1007/s43681-024-00569-3
[74] Pavlidis, A. (2025). High-risk AI systems under the EU AI Act: Implications for financial governance. European Business Organization Law Review, 26(1), 115–134. https://doi.org/10.1007/s40804-024-00321-5
[75] del Rio, M., Costa, S., & Evans, T. (2025). GDPR-compliant AI systems and cross-border data governance in financial services. Information & Computer Security, 33(1), 91–109. https://doi.org/10.1108/ICS-06-2024-0098
[76] Markarian, A., Singh, P., & Zhao, H. (2025). Algorithmic lending, fairness, and explainability in AI-driven credit scoring systems. Finance Research Letters, 66, Article 105377. https://doi.org/10.1016/j.frl.2024.105377
[77] Zeranski, T., & Sancak, A. (2021). Supervisory technology (SupTech): Opportunities and challenges for financial regulators. Bank for International Settlements Working Papers, 1014, 1–35. https://doi.org/10.2139/ssrn.3964201
[78] Barrière, S. (2021). SupTech applications in financial market supervision and liquidity monitoring. Journal of Financial Regulation, 7(2), 211–229. https://doi.org/10.1093/jfr/fjab008
[79] Fachsandy, M. (2025). Supervisory technology and algorithmic governance: A bibliometric perspective. Scientometrics, 130(4), 2411–2432. https://doi.org/10.1007/s11192-025-05312-6
[80] Yang, D., Li, K., & Wong, J. (2025). Algorithmic underwriting and governance challenges in InsurTech systems. Insurance Markets and Companies, 16(1), 58–74. https://doi.org/10.21511/ins.16(1).2025.05
[81] Marr, B. (2025). Artificial intelligence and automated claims processing in insurance governance. Forbes Technology Council. https://www.forbes.com/sites/bernardmarr
[82] Arner, D. W., Barberis, J., & Buckley, R. P. (2019). FinTech for financial inclusion: A framework for digital financial transformation. University of Hong Kong Faculty of Law Research Paper, 2019/001, 1–29. https://doi.org/10.2139/ssrn.3245287
[83] Truby, J. (2020). FinTech and AI for financial inclusion: The role of algorithmic governance. Law and Financial Markets Review, 14(1), 7–18. https://doi.org/10.1080/17521440.2020.1725274
