Managing Risks In Fintech: Applications And Challenges Of Artificial Intelligence-Based Risk Management
Main Article Content
Benediktus Rolando
Herry Mulyono
Artificial Intelligence has become a transformative technology in the field of financial technology, leveraging advanced algorithms and machine learning to identify risks and make informed decisions. However, its widespread adoption presents new challenges related to ethical use, data privacy, security concerns, potential bias, and discrimination. This study aims to explore the benefits of AI-based risk management in Fintech while highlighting associated challenges and providing recommendations. This research utilises the systematic review methodology to analyse existing literature and identify important patterns, gaps, and areas for further investigation. The study utilised data gathered from the Scopus database to obtain credible scholarly materials. Research data was collected from a variety of countries including the United States, China, European nations, and other Asian countries in order to develop a comprehensive understanding of AI-based risk management on a global scale. The findings highlight the crucial role of ethical considerations in implementing AI-based risk management systems to ensure fairness, transparency, and accountability. Moreover, the fintech industry needs to establish strong data protection measures and address issues related to bias and discrimination in order to instil trust and uphold public confidence in AI-based risk management. Future research should emphasise assessing the effectiveness of different algorithms and approaches while also examining potential regulatory frameworks and legal implications associated with AI-based risk management strategies.
Al-Gasawneh, J. A., Al-Hawamleh, A. M., Alorfi, A., & Al-Rawashdeh, G. (2022). Moderating the role of the perceived security and endorsement on the relationship between perceived risk and intention to use the artificial intelligence in financial services. International Journal of Data and Network Science, 6(3), 743–752. https://doi.org/10.5267/j.ijdns.2022.3.007
Ali, M. S., Swiety, I. A., & Mansour, M. H. (2022). EVALUATING THE ROLE OF ARTIFICIAL INTELLIGENCE IN THE AUTOMATION OF THE BANKING SERVICES INDUSTRY: EVIDENCE FROM JORDAN. Humanities and Social Sciences Letters, 10(3), 383–393. https://doi.org/10.18488/73.v10i3.3090
Almustafa, E., Assaf, A., & Allahham, M. (2023). IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE FOR FINANCIAL PROCESS INNOVATION OF COMMERCIAL BANKS. Revista de Gestao Social e Ambiental, 17(9). https://doi.org/10.24857/rgsa.v17n9-004
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. https://doi.org/10.1002/jsc.2404
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Beasley, M., Branson, B., & Pagach, D. (2023). An Evolving Risk Landscape: Insights from a Decade of Surveys of Executives and Risk Professionals. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010029
Be̜dowska-Sójka, B., Kliber, A., & Laidroo, L. (2023). Has the pandemic changed the relationships between fintechs and banks? Operations Research and Decisions, 33(4), 15–33. https://doi.org/10.37190/ord230402
Bhatia, A., Chandani, A., & Chhateja, J. (2020). Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context. Journal of Behavioral and Experimental Finance, 25. https://doi.org/10.1016/j.jbef.2020.100281
Boreiko, D., & Massarotti, F. (2020). How Risk Profiles of Investors Affect Robo-Advised Portfolios. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00060
Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable Machine Learning in Credit Risk Management. Computational Economics, 57(1), 203–216. https://doi.org/10.1007/s10614-020-10042-0
Cangemi, M. P., & Taylor, P. (2018). HARNESSING ARTIFICIAL INTELLIGENCE TO DELIVER REAL-TIME INTELLIGENCE AND BUSINESS PROCESS IMPROVEMENTS. EDPACS, 57(4), 1–6. https://doi.org/10.1080/07366981.2018.1444007
Cao, L. (2022). Artificial intelligence in finance: A review. Journal of Financial Data Science, 4(1), 1-18. doi:10.2139/ssrn.4026127
Careglio, C., et al. (2022). Ethical guidelines for AI governance. Journal of Business Ethics, 151(2), 289-306. doi:10.1007/s10551-021-04708-5
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-August-2016, 785–794. https://doi.org/10.1145/2939672.2939785
El Hajj, M., & Hammoud, J. (2023). Unveiling the Influence of Artificial Intelligence and Machine Learning on Financial Markets: A Comprehensive Analysis of AI Applications in Trading, Risk Management, and Financial Operations. Journal of Risk and Financial Management, 16(10). https://doi.org/10.3390/jrfm16100434
Faccia, A. (2023). National Payment Switches and the Power of Cognitive Computing against Fintech Fraud. Big Data and Cognitive Computing, 7(2). https://doi.org/10.3390/bdcc7020076
Friedman, J., Hastie, T., & Tibshirani, R. (2000). ADDITIVE LOGISTIC REGRESSION: A STATISTICAL VIEW OF BOOSTING. In The Annals of Statistics (Vol. 28, Issue 2).
Garg, G., Shamshad, M., Gauhar, N., Tabash, M. I., Hamouri, B., & Daniel, L. N. (2023). A Bibliometric Analysis of Fintech Trends: An Empirical Investigation. In International Journal of Financial Studies (Vol. 11, Issue 2). MDPI. https://doi.org/10.3390/ijfs11020079
Gasawneh, J. A., Al-Hawamleh, A. M., Alorfi, A., & Al-Rawashdeh, G. (2022). Moderating the role of the perceived security and endorsement on the relationship between perceived risk and intention to use the artificial intelligence in financial services. International Journal of Data and Network Science, 6(3), 743–752. https://doi.org/10.5267/j.ijdns.2022.3.007
Genovesi, S., Mönig, J. M., Schmitz, A., Poretschkin, M., Akila, M., Kahdan, M., Kleiner, R., Krieger, L., & Zimmermann, A. (2023). Standardizing fairness-evaluation procedures: interdisciplinary insights on machine learning algorithms in creditworthiness assessments for small personal loans. AI and Ethics. https://doi.org/10.1007/s43681-023-00291-8
Giudici, P. (2018). Financial data science. Statistics and Probability Letters, 136, 160–164. https://doi.org/10.1016/j.spl.2018.02.024
Giudici, P., & Raffinetti, E. (2023). SAFE Artificial Intelligence in finance. Finance Research Letters, 56. https://doi.org/10.1016/j.frl.2023.104088
Giudici, P., Hadji-Misheva, B., & Spelta, A. (2020). Network based credit risk models. Quality Engineering, 32(2), 199–211. https://doi.org/10.1080/08982112.2019.1655159
Grassi, L., & Lanfranchi, D. (2022). RegTech in public and private sectors: the nexus between data, technology and regulation. Journal of Industrial and Business Economics, 49(3), 441–479. https://doi.org/10.1007/s40812-022-00226-0
Guerra, P., Castelli, M., & Côrte-Real, N. (2022). Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System. Risks, 10(4). https://doi.org/10.3390/risks10040071
Haddad, C., & Hornuf, L. (2023). How do fintech start-ups affect financial institutions’ performance and default risk? European Journal of Finance, 29(15), 1761–1792. https://doi.org/10.1080/1351847X.2022.2151371
Jain, R., Kumar, S., Sood, K., Grima, S., & Rupeika-Apoga, R. (2023). A Systematic Literature Review of the Risk Landscape in Fintech. In Risks (Vol. 11, Issue 2). MDPI. https://doi.org/10.3390/risks11020036
Jiang, D., Ni, Z. X., Chen, Y., Chen, X., & Na, C. (2023). Influence of Financial Shared Services on the Corporate Debt Cost under Digitalisation. Sustainability (Switzerland), 15(1). https://doi.org/10.3390/su15010428
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. doi:10.1038/s42256-019-0088-2
Johnson, K. N., Stafford Professor of Law, M., Dean, A., MacLeod Heminway, J., Fisch, J., Hui Kim, S., Simpson, A., & Carbone, J. (n.d.). Automating the Risk of Bias. https://perma.cc/4AJN-LVNE].
Khan, H. H., Khan, S., & Ghafoor, A. (2023). Fintech adoption, the regulatory environment and bank stability: An empirical investigation from GCC economies. Borsa Istanbul Review, 23(6), 1263–1281. https://doi.org/10.1016/j.bir.2023.10.010
Khan, S., & Al-Harby, A. S. A. (2022). THE USE OF FINTECH AND ITS IMPACT ON FINANCIAL INTERMEDIATION – A COMPARISON OF SAUDI ARABIA WITH OTHER GCC ECONOMIES. Intellectual Economics, 16(2), 26–43. https://doi.org/10.13165/IE-22-16-2-02
Klius, Y., Ivchenko, Y., Izhboldina, A., & Ivchenko, Y. (2020). International approaches to organising an internal control system at an enterprise in the digital era. Economic Annals-XXI, 185(9–10), 133–143. https://doi.org/10.21003/EA.V185-13
Larkin, C., Drummond, C., Árvai, J., & Ross, S. M. (n.d.). Paging Dr. JARVIS! Do people accept risk management advice from artificial intelligence in consequential decision contexts? Erb Institute for Global Sustainable Enterprise. https://www.accenture.com/_acnmedia/pdf-
Liu, Y., et al. (2023). Artificial intelligence in finance: Applications and future directions. Journal of Financial Innovation, 9(1), 1-15. doi:10.2139/ssrn.4026127
Mishchenko, S., Naumenkova, S., Mishchenko, V., & Dorofeiev, D. (2021). Innovation risk management in financial institutions. Investment Management and Financial Innovations, 18(1), 191–203. https://doi.org/10.21511/imfi.18(1).2021.16
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences of the United States of America, 116(44), 22071–22080. https://doi.org/10.1073/pnas.1900654116
Nijjer, S., Sood, K., Grima, S., Rupeika-Apoga, R., & Varma, P. (2022). Thematic Analysis of Financial Technology (Fintech) Influence on the Banking Industry. Risks, 10, 186. https://doi.org/10.3390/risks
Peters, G. W., Clark, G., Thirlwell, J., & Kulwal, M. (2018). Global perspectives on operational risk management and practice: a survey by the institute of operational risk (IOR) and the center for financial professionals (ceFPro). Journal of Operational Risk, 13(4), 47–88. https://doi.org/10.21314/JOP.2018.215
Piotrowski, D. (2023). Privacy frontiers in customers’ relations with banks. Economics and Business Review, 9(1), 119–141. https://doi.org/10.18559/ebr.2023.1.5
Praveenraj, J. (2023). Explainable AI: A review. Journal of Artificial Intelligence Research, 67, 1-28. doi:10.2139/ssrn.4026127 used: emphasis on transparency, fairness, and accountability in AI decision-making
Prisznyák, A. (2022). Bankrobotika: Mesterséges intelligencia és gépi tanulás alapú banki kockázatkezelés Pénzmosás és terrorizmusfinanszírozás megakadályozása. Public Finance Quarterly, 67(2), 293–308. https://doi.org/10.35551/PSZ_2022_2_8
Roxana MOSTEANU, N., & Faccia, A. (n.d.). Digital Systems and New Challenges of Financial Management – FinTech, XBRL, Blockchain and Cryptocurrencies. Journal of Management Systems-Quality Access to Success, 21(174), 159–166.
Sahabuddin, R., Ibrahim Rauf , D., Putri, S. G., Nurlina, N., Muchtar, M. F., Ilmi, H. N., & Sulfikar, M. F. (2024). The ability to analyze the latest market trends in increasing sales in MSMEs Bouqetcru . Journal of Management Science (JMAS), 7(1), 328-332. https://doi.org/10.35335/jmas.v7i1.371
Shaharruddin, S., & Musa, M. M. (2022). A Future Malaysian Banking Landscape in Embracing IR4.0: A New Leadership Model. Journal of Advanced Research in Applied Sciences and Engineering Technology, 28(3), 264–271. https://doi.org/10.37934/araset.28.3.264271
Shrestha, K., Naysary, B., & Philip, S. S. S. (2023). Fintech market efficiency: A multifractal detrended fluctuation analysis. Finance Research Letters, 54. https://doi.org/10.1016/j.frl.2023.103775
Turek, J., Ocicka, B., Rogowski, W., & Jefmański, B. (2023). The role of Industry 4.0 technologies in driving the financial importance of sustainability risk management. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(4), 1009–1044. https://doi.org/10.24136/eq.2023.032
Versal, N., Erastov, V., Balytska, M., & Honchar, I. (2022). Digitalization Index: Case for Banking System. Statistika, 102(4), 426–442. https://doi.org/10.54694/STAT.2022.16
Vučinić, M., & Luburić, R. (2022). Fintech, Risk-Based Thinking and Cyber Risk. Journal of Central Banking Theory and Practice, 11(2), 27–53. https://doi.org/10.2478/jcbtp-2022-0012
Zhang, X. (2021). Application of data mining and machine learning in management accounting information system. Journal of Applied Science and Engineering (Taiwan), 24(5), 813–820. https://doi.org/10.6180/jase.202110_24(5).0018
