Risk Management, Digital Innovation, and Regulatory Frameworks in Banking and Finance
DOI:
https://doi.org/10.70301/JOUR/SBS-JABR/2025/13/2/2Keywords:
Risk Management;, Digital Finance;, Sentiment Analysis;, Regulatory Frameworks:, Financial Stability;Abstract
This research examines the effects of digital innovations, regulatory frameworks, and advanced risk management on banking system stability, alongside social media sentiment’s impact on electric vehicle companies’ financial choices. Employing a mixed-methods approach analyzing financial documents, licensure records, and social media data, the study uses econometric measurement and qualitative framework inspection. Sentiment analysis of electric vehicle data from 2022-2023, utilizing NLP tools, reveals an increasing positive attitude towards EVs, although security and infrastructure concerns remain. Key banking findings indicate that AI-driven risk assessment algorithms achieve an 89% measurement success rate, surpassing traditional methods at 72%. Furthermore, GPU acceleration in AI-based financial models improves execution efficiency by a factor of four. Sentiment analysis shows a strong positive correlation (0.85) between the sentiment index and market stability indicators. Digital Twin simulations demonstrate a 91% accuracy rate in forecasting financial crises, significantly higher than the 76% accuracy of historical models. The research suggests that banks implementing digital transformation and regulatory adaptability achieve stronger financial stability, with AI for risk management improving organizational performance. Policymakers should create adaptable fintech regulations and utilize robust risk control methods. Emerging tech firms and financial institutions should leverage sentiment trends for strategic planning. This research establishes novel connections between traditional finance risk management, digital transformation, and sentiment-driven analysis, offering a sustainable framework for stability and technology adoption, and proposes new methodologies for risk and consumer pattern analysis.
References
Acecloud.ai (2024). How GPU-accelerated AI is dealing with financial risk management. Available at: https://acecloud.ai.
Ahuchogu, M., Sanyaolu, T., & Adeleke, A. (2024). Balancing innovation with risk management in digital banking transformation for enhanced customer satisfaction and security. International Journal of Management & Entrepreneurship Research, 6(9), 3022–3049. https://doi.org/10.51594/ijmer.v6i9.1566.
Altman, E. I., Sabato, G., & Wilson, N. (2020). The value of machine learning in predicting financial distress. Journal of Banking & Finance, 112, 105733. https://doi.org/10.1016/j.jbankfin.2019.105733.
American Bankers Association, (2024). AI's rapid rise compels smart risk management for banks. [online] Available at: https://bankingjournal.aba.com/2024/11/ais-rapid-rise-compels-smart-risk-management-for-banks/ [Accessed 08 Jan. 2025].
Arner, D. W., Barberis, J., & Buckley, R. P. (2019). FinTech, RegTech, and the reconceptualization of financial regulation. Northwestern Journal of International Law & Business, 37(3), 371-413.
Bankingdive.com (2024). Digital Twins: Catalyzing AI for financial risk management. Available at: https://www.bankingdive.com.
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2017). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602–613. https://doi.org/10.1016/j.dss.2010.08.008
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. https://doi.org/10.1016/j.jocs.2010.12.007
Brown, T., & Taylor, M. (2020). Algorithmic biases and financial discrimination: An emerging risk. Journal of Financial Regulation and Compliance, 28(3), 400–414. https://doi.org/10.1108/JFRC-03-2020-0025
Brynjolfsson, E., & McAfee, A. (2017). The Business of Artificial Intelligence: What It Can — and Cannot — Do for Your Organization. Harvard Business Review. https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence
Bussmann, N., Giudici, P., & Marinelli, D. (2020). Machine learning in finance: How AI and risk analytics can revolutionize risk management. The European Journal of Finance, 26(4-5), 302-317. https://doi.org/10.1080/1351847X.2019.1662828
Chen, Y., González, L., & Weber, M. (2024). Artificial intelligence and financial soundness: Evidence from European banks. Journal of Financial Stability, 67, 101095. https://doi.org/10.1016/j.jfs.2024.101095
Chen, M. A., Wu, Q., & Yang, B. (2022). How does social media sentiment impact stock returns? Evidence from the electric vehicle industry. Finance Research Letters, 45, 102135. https://doi.org/10.1016/j.frl.2021.102135
Čihák, M., & Schaeck, K. (2010). How well do aggregate bank ratios identify banking problems? Journal of Financial Stability, 6(3), 130–144. https://doi.org/10.1016/j.jfs.2010.02.001
Davis, L. (2022). Financial inclusion through FinTech: Opportunities and challenges. International Journal of Finance and Economics, 27(2), 1420–1435. https://doi.org/10.1002/ijfe.2231
Davis, P., & Smith, R. (2019). Transparency and trust in AI-driven banking systems. Journal of Banking and Technology, 13(1), 45–58. https://doi.org/10.1016/j.banktech.2018.12.003
DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101
Ecbis.net (2023). Managing risks in fintech: Applications and challenges of artificial intelligence. Available at: https://ecbis.net.
European Central Bank, (2024). The rise of artificial intelligence: benefits and risks for financial stability. [online] Available at: https://www.ecb.europa.eu/press/financial-stability-publications/fsr/special/html/ecb.fsrart202405_02~58c3ce5246.en.html [Accessed 14 Jan. 2025].
European Central Bank. (2022). Digital finance and regulatory framework report. https://www.ecb.europa.eu
Evans, B. (2020). Regulating disruption: Balancing innovation and stability in the FinTech era. Journal of Financial Regulation, 6(1), 90–110. https://doi.org/10.1093/jfr/fjaa001
Ey.com (2023). Banking risks from AI and machine learning. Available at: https://www.ey.com.
Fepbl.com (2023). Leveraging artificial intelligence for enhanced risk management in financial services. Available at: https://www.fepbl.com.
Financial Stability Board, (2024). The Financial Stability Implications of Artificial Intelligence. [online] Available at: https://www.fsb.org/2024/11/the-financial-stability-implications-of-artificial-intelligence/ [Accessed 21 Dec. 2024].
Financial Stability Board. (2022). FinTech and market structure in financial services. https://www.fsb.org
Financial Times, (2025). Do androids dream of financial crises? [online] Available at: https://www.ft.com/content/fe64c3b8-4097-48b5-821d-57040b9ec076 [Accessed 10 Feb. 2025].
Fintech. global (2024). How will risk management practices evolve in the Gen AI era? Available at: https://fintech.global.
FinTech Global. (2024). The impact of AI on banking risk management: Current trends and future directions. FinTech Global Insights. Retrieved from https://fintech.global
Foster, A. (2021). Flexible regulatory frameworks for financial innovation. Journal of Financial Innovation, 7(2), 65–80. https://doi.org/10.1016/j.fininnov.2021.05.004
Frontiersin.org (2020). Explainable AI in fintech risk management. Available at: https://www.frontiersin.org.
Frontiers in Artificial Intelligence, (2020). Explainable AI in Fintech Risk Management. [online] Available at: https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2020.00026/full [Accessed 10 Dec. 2024].
FT.com (2024). Banks’ use of AI could be included in stress tests. Available at: https://www.ft.com.
Goodell, J. W., Goutte, S., & Jareño, F. (2021). COVID-19 and finance: Agendas for future research. Finance Research Letters, 38, 101512. https://doi.org/10.1016/j.frl.2020.101512
Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2017). On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial Services. Journal of Management Information Systems, 35(1), 220–265. https://doi.org/10.1080/07421222.2018.1440766
Gov.uk (2023). Explainable AI for credit risk management. Available at: https://www.gov.uk.
Green, S. (2021). Cybersecurity vulnerabilities in digital finance: A growing concern. Journal of Information Security and Applications, 58, 102784. https://doi.org/10.1016/j.jisa.2021.102784
Hutto, C. J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the Eighth International Conference on Weblogs and Social Media (ICWSM-14), 216–225. https://ojs.aaai.org/index.php/ICWSM/article/view/14550
International Monetary Fund, (2021). Opportunities and Risks of Artificial Intelligence in Finance. [online] Available at: https://www.elibrary.imf.org/view/journals/087/2021/024/article-A001-en.xml [Accessed 12 Dec. 2024].
IMF (2023). Generative artificial intelligence in finance: Risk considerations. Available at: https://www.imf.org.
Johnson, M., & Wang, Y. (2021). AI-enabled fraud detection systems: Advances and limitations. Journal of Financial Crime, 28(4), 1035–1050. https://doi.org/10.1108/JFC-12-2020-0245
Kou, G., Yang, P., Xiao, F., Chen, Z., & Alsaadi, F. E. (2021). Machine learning methods for systemic risk analysis in financial sectors. Technological Forecasting and Social Change, 163, 120412. https://doi.org/10.1016/j.techfore.2020.120412
KTH Diva (2022). The impact of AI on banks' risk management approach. Available at: https://kth.diva-portal.org.
Kumar, A. (2020). Navigating the black box: Challenges of AI transparency in finance. Journal of Risk and Financial Management, 13(12), 326. https://doi.org/10.3390/jrfm13120326
Lee, J., & Park, H. (2023). FinTech innovation and asset quality: Evidence from South Korean commercial banks. Journal of Banking and Finance, 149, 106804. https://doi.org/10.1016/j.jbankfin.2023.106804
Lee, J. (2021). FinTech disruption and its impact on banking sector stability. Financial Innovation, 7(1), 1–14. https://doi.org/10.1186/s40854-021-00257-3
Leo, M., & Sharma, R. (2019). Advances in AI for banking risk management. Journal of Risk Management in Financial Institutions, 12(2), 103-117.
Leo, M., & Sharma, A. (2019). Machine learning applications in financial risk management. Journal of Risk and Financial Management, 12(3), 150. https://doi.org/10.3390/jrfm12030150
Liu, B., Li, X., & Yu, P. S. (2020). Sentiment analysis: Mining opinions, sentiments, and emotions. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(2), e1335. https://doi.org/10.1002/widm.1335
Loria, S. (2018). TextBlob: Simplified text processing (Version 0.15.3) [Computer software]. https://textblob.readthedocs.io/en/dev/
Nasdaq (2024). Nasdaq’s AI integration streamlines risk management for banks and insurers. Available at: https://fintech.global.
Orbis BankFocus. (2023). Global financial data on banks. Bureau van Dijk. https://www.bvdinfo.com
Patel, R. (2022). Rethinking financial regulation in the AI era. Journal of Financial Regulation and Compliance, 30(1), 23–41. https://doi.org/10.1108/JFRC-08-2021-0120
Paul, L., & Rena, R. (2024a). The role of digital crowdfunding platforms in democratizing global entrepreneurship. Journal of Economic Research & Business Administration, (3)149, 46–58. Retrieved from https://be.kaznu.kz/index.php/math/article/view/2862
Paul, L., & Rena, R. (2024b). Fin-Tech Meets Block Chain: Shaping the future of finance and economic innovation. Research World-International Refereed Social Science Journal, 15(2), 142–150. Retrieved from https://www.researchersworld.com/index.php/rworld/article/view/2335
Philippon, T. (2019). The FinTech opportunity. National Bureau of Economic Research, Working Paper 22476. https://doi.org/10.3386/w22476.
Rena, R. (2006). Financial Institutions in Eritrea, Dar es Salaam: New Africa Press; pages: 120.
ResearchGate, (2024). AI-Driven Risk Management Strategies in Financial Technology. [online] Available at: https://www.researchgate.net/publication/382207578_AI-Driven_Risk_Management_Strategies_in_Financial_Technology [Accessed 18 Dec. 2024].
Reserve Bank of India. (2022). Report on trends and progress of banking in India. https://www.rbi.org.in
Reuters, (2024). New York Department of Financial Services provides AI cybersecurity guidance: what you need to know. [online] Available at: https://www.reuters.com/legal/legalindustry/new-york-department-financial-services-provides-ai-cybersecurity-guidance-what-2024-11-15/ [Accessed 08 Dec. 2024].
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Smith, J., Thomas, L., & Zhang, H. (2020). Predicting credit risk: A machine learning approach. European Journal of Operational Research, 281(2), 402–414. https://doi.org/10.1016/j.ejor.2019.08.030
South African Reserve Bank. (2022). Financial stability review. https://www.resbank.co.za
The Times, 2024. Use of AI for checks could help the City cut money laundering. [online] Available at: https://www.thetimes.co.uk/article/use-of-ai-for-checks-could-help-city-cut-money-laundering-enterprise-network-6vng7qfp6 [Accessed 21 Dec. 2024].
The Wall Street Journal, (2025). Can a Computer Learn to Speak Trader? [online] Available at: https://www.wsj.com/articles/can-a-computer-learn-to-speak-trader-7695fbc9 [Accessed 10 Feb. 2025].
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
Zhang, L., & Liu, Y. (2022). The impact of social media sentiment on stock market returns: Evidence from the electric vehicle industry. Finance Research Letters, 47, 102617. https://doi.org/10.1016/j.frl.2022.102617
Zhang, X., & Zohar, D. (2022). Regulatory challenges in AI-driven financial markets. Journal of Financial Regulation, 8(1), 75-98. https://doi.org/10.1093/jfr/fjab013
Zhang, D., & Broadstock, D. C. (2020). Impact of FinTech on financial stability: Evidence from China. Finance Research Letters, 36, 101637. https://doi.org/10.1016/j.frl.2019.101637
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