Author information:
Gergely Lülök https://orcid.org/0009-0006-5080-7314: Budapest University of Technology and Economics, PhD Student. E-mail: lulok.gergely@edu.bme.hu
Zoltán Sebestyén https://orcid.org/0000-0002-2382-8797: Budapest University of Technology and Economics, Associate Professor. E-mail: sebestyen.zoltan@gtk.bme.hu
Abstract:
The study examines the latest trends in the application of artificial intelligence (AI) in the banking sector, with a focus on bank failure prediction, risk management and customer relationship optimisation. The research is based on a systematic literature search of relevant publications in the Scopus and Web of Science databases, using the PRISMA methodology for source selection and analysis. The results show that Unsupervised Learning Models dominate in bankruptcy prediction and risk analysis, while Natural Language Processing and Deep Learning techniques are mainly focused on improving customer relationships and increasing bank efficiency. The research shows that AI is playing an increasingly important role in banking decision-making, but that the different application areas face different regulatory and ethical challenges. The results underline the importance for financial institutions to improve the transparency and interpretability of AI and to develop adaptive regulatory frameworks to balance innovation and security.
Cite as (APA):
Lülök, G., & Sebestyén, Z. (2025). Latest Trends in the Use of Artificial Intelligence in the Banking Sector. Financial and Economic Review, 24(2), 47–72. https://doi.org/10.33893/FER.24.2.47
Column:
Study
Journal of Economic Literature (JEL) codes:
C10, G21, O33
Keywords:
artificial intelligence, banking sector, financial services, trend analysis
References:
Al-Hawamdeh, M.M. – AlShaer, S.A. (2022): Artificial intelligence applications as a modern trend to achieve organizational innovation in Jordanian commercial banks. Journal of Asian Finance, Economics and Business, 9(3): 257–263. https://doi.org/10.13106/jafeb.2022.vol9.no3.0257
Almubaydeen, T. – Alkabbji, R. – Fleifil, M.K.A. (2025): Effect of applying artificial intelligence on the quality of accounting information in commercial banks (a field study). In: Musleh Al-Sartawi, A.M.A. – Al-Okaily, M. – Al-Qudah, A.A. – Shihadeh, F. (eds): Studies in Systems, Decision and Control. Vol. 572, Springer, Cham., pp. 787–803. https://doi.org/10.1007/978-3-031-76011-2_56
Alonso-Robisco, A. – Carbó, J.M. (2022): Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio. International Review of Financial Analysis, 84, 102372. https://doi.org/10.1016/j.irfa.2022.102372
Asmar, M. – Tuqan, A. (2024): Integrating machine learning for sustaining cybersecurity in digital banks. Heliyon, 10(17), e37571. https://doi.org/10.1016/j.heliyon.2024.e37571
Bagó, P. (2023): The Potential of Artificial Intelligence in Finance. Economy and Finance, 10(1): 20–37. https://doi.org/10.33908/EF.2023.1.2
Benedek, B. – Nagy, B.Z. (2023): Traditional versus AI-Based Fraud Detection: Cost Efficiency in the Field of Automobile Insurance. Financial and Economic Review, 22(2): 77–98. https://doi.org/10.33893/FER.22.2.77
Bockel-Rickermann, C. – Verboven, S. – Verdonck, T. – Verbeke, W. (2025): Can causal machine learning reveal individual bid responses of bank customers? — A study on mortgage loan applications in Belgium. Decision Support Systems, 190, 114378. https://doi.org/10.1016/j.dss.2024.114378
Bolívar, F. – Duran, M.A. – Lozano-Vivas, A. (2023): Business model contributions to bank profit performance: A machine learning approach. Research in International Business and Finance, 64, 101870. https://doi.org/10.1016/j.ribaf.2022.101870
Bonaparte, Y. (2024): Artificial Intelligence in Finance: Valuations and Opportunities. Finance Research Letters, 60, 104851. https://doi.org/10.1016/j.frl.2023.104851
Boncz, B. – Szabó, Zs.R. (2022): A mesterséges intelligencia munkaerő-piaci hatásai – Hogyan készüljünk fel? (The Effects of Artificial Intelligence on the Labor Market: How to Prepare?). Vezetéstudomány / Budapest Management Review, 53(2): 68–80. https://doi.org/10.14267/veztud.2022.02.06
Chishti, M.Z. – Dogan, E. – Binsaeed, R.H. (2024): Can artificial intelligence and green finance affect economic cycles? Technological Forecasting and Social Change, 209, 123740. https://doi.org/10.1016/j.techfore.2024.123740
Cintamür, İ.G. (2024): Acceptance of artificial intelligence devices in banking services: Moderation role of technology anxiety and risk aversion. International Journal of Bank Marketing, 42(7): 2143–2176. https://doi.org/10.1108/ijbm-10-2023-0563
Domokos, A. – Sajtos, P. (2024): Artificial Intelligence in the Financial Sector – Innovation and Risks. Financial and Economic Review, 23(1): 155–166. https://hitelintezetiszemle.mnb.hu/en/fer-23-1-fa1-domokos-sajtos
Durongkadej, I. – Hu, W. – Wang, H.E. (2024): How artificial intelligence incidents affect banks and financial services firms? A study of five firms. Finance Research Letters, 70, 106279. https://doi.org/10.1016/j.frl.2024.106279
Fraisse, H. – Laporte, M. (2022): Return on investment on artificial intelligence: The case of bank capital requirement. Journal of Banking & Finance, 138, 106401. https://doi.org/10.1016/j.jbankfin.2022.106401
Gogas, P. – Papadimitriou, T. – Agrapetidou, A. (2018): Forecasting bank failures and stress testing: A machine learning approach. International Journal of Forecasting, 34(3): 440–455. https://doi.org/10.1016/j.ijforecast.2018.01.009
González-Carrasco, I. – Jiménez-Márquez, J.L. – López-Cuadrado, J.L. – Ruiz-Mezcua, B. (2019): Automatic detection of relationships between banking operations using machine learning. Information Sciences, 485: 319–346. https://doi.org/10.1016/j.ins.2019.02.030
González-González, J. – García-Méndez, S. – De Arriba-Pérez, F. – González-Castaño, F.J. – Barba-Seara, Ó. (2022): Explainable automatic industrial carbon footprint estimation from bank transaction classification using natural language processing. IEEE Access, 10: 126326–126338. https://doi.org/10.1109/access.2022.3226324
Harkácsi, G.J. – Szegfű, L.P. (2021): The Role of the Compliance Function in the Financial Sector in the Age of Digitalisation, Artificial Intelligence and Robotisation. Financial and Economic Review, 20(1): 152–170. https://doi.org/10.33893/FER.20.1.152170
Hentzen, J.K. – Hoffmann, A. – Dolan, R. – Pala, E. (2021): Artificial intelligence in customer-facing financial services: A systematic literature review and agenda for future research. International Journal of Bank Marketing, 40(6): 1299–1336. https://doi.org/10.1108/ijbm-09-2021-0417
Heß, V.L. – Damásio, B. (2025): Machine learning in banking risk management: Mapping a decade of evolution. International Journal of Information Management Data Insights, 5(1), 100324. https://doi.org/10.1016/j.jjimei.2025.100324
Hu, W. – Shao, C. – Zhang, W. (2025): Predicting U.S. bank failures and stress testing with machine learning algorithms. Finance Research Letters, 75, 106802. https://doi.org/10.1016/j.frl.2025.106802
Hussein Sayed, E. – Alabrah, A. – Hussein Rahouma, K. – Zohaib, M. – Badry, R.M. (2024): Machine learning and deep learning for loan prediction in banking: Exploring ensemble methods and data balancing. IEEE Access, 12: 193997–194019. https://doi.org/10.1109/access.2024.3509774
Ikhsan, R.B. – Fernando, Y. – Prabowo, H. – Yuniarty, Y. – Gui, A. – Kuncoro, E.A. (2025): An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust. Digital Business, 5(1), 100103. https://doi.org/10.1016/j.digbus.2024.100103
Katsafados, A.G. – Leledakis, G.N. – Pyrgiotakis, E.G. – Androutsopoulos, I. – Fergadiotis, M. (2024): Machine learning in bank merger prediction: A text-based approach. European Journal of Operational Research, 312(2): 783–797. https://doi.org/10.1016/j.ejor.2023.07.039
Khaled Alarfaj, F. – Shahzadi, S. (2025): Enhancing fraud detection in banking with deep learning: Graph neural networks and autoencoders for real-time credit card fraud prevention. IEEE Access, 13: 20633–20646. https://doi.org/10.1109/access.2024.3466288
Khosravi, T. – Al Sudani, Z.M. – Oladnabi, M. (2023): To what extent does ChatGPT understand genetics? Innovations in Education and Teaching International, 61(6): 1320–1329. https://doi.org/10.1080/14703297.2023.2258842
Klein, T. – Walther, T. (2024): Advances in explainable artificial intelligence (xAI) in finance. Finance Research Letters, 70, 106358. https://doi.org/10.1016/j.frl.2024.106358
Königstorfer, F. – Thalmann, S. (2020): Applications of artificial intelligence in commercial banks – A research agenda for behavioral finance. Journal of Behavioral and Experimental Finance, 27, 100352. https://doi.org/10.1016/j.jbef.2020.100352
Kruppa, J. – Schwarz, A. – Arminger, G. – Ziegler, A. (2013): Consumer credit risk: Individual probability estimates using machine learning. Expert Systems with Applications, 40(13): 5125–5131. https://doi.org/10.1016/j.eswa.2013.03.019
Lagasio, V. – Pampurini, F. – Pezzola, A. – Quaranta, A.G. (2022): Assessing bank default determinants via machine learning. Information Sciences, 618: 87–97. https://doi.org/10.1016/j.ins.2022.10.128
Le, H.H. – Viviani, J.-L. (2018): Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios. Research in International Business and Finance, 44: 16–25. https://doi.org/10.1016/j.ribaf.2017.07.104
Lee, J.-C. – Chen, X. (2022): Exploring users’ adoption intentions in the evolution of artificial intelligence mobile banking applications: The intelligent and anthropomorphic perspectives. International Journal of Bank Marketing, 40(4): 631–658. https://doi.org/10.1108/ijbm-08-2021-0394
Lin, S. – Song, D. – Cao, B. – Gu, X. – Li, J. (2025): Credit risk assessment of automobile loans using machine learning-based SHapley Additive exPlanations approach. Engineering Applications of Artificial Intelligence, 147, 110236. https://doi.org/10.1016/j.engappai.2025.110236
López Lázaro, J. – Barbero Jiménez, Á. – Takeda, A. (2018): Improving cash logistics in bank branches by coupling machine learning and robust optimization. Expert Systems with Applications, 92: 236–255. https://doi.org/10.1016/j.eswa.2017.09.043
Lu, Y. – Zhao, Z. – Tian, Y. – Zhan, M. (2024): How does the economic structure break change the forecast effect of money and credit on output? Evidence based on machine learning algorithms. Pacific-Basin Finance Journal, 84, 102325. https://doi.org/10.1016/j.pacfin.2024.102325
Ma, C.-Q. – Liu, X. – Klein, T. – Ren, Y.-S. (2025): Decoding the nexus: How fintech and AI stocks drive the future of sustainable finance. International Review of Economics & Finance, 98, 103877. https://doi.org/10.1016/j.iref.2025.103877
Mercadier, M. – Tarazi, A. – Armand, P. – Lardy, J.-P. (2025): Monitoring bank risk around the world using unsupervised learning. European Journal of Operational Research, 324(2): 590–615. https://doi.org/10.1016/j.ejor.2025.01.036
Met, I. – Erkoç, A. – Seker, S.E. (2023): Performance, efficiency, and target setting for bank branches: Time series with automated machine learning. IEEE Access, 11: 1000–1010. https://doi.org/10.1109/access.2022.3233529
Mishra, M.K. – Pattanayak, M. – Shankar, A.U. – Murthy, G.V.K. – Singh, S. (2023): Impact of artificial intelligence on human behaviour & well-being – An empirical analysis. Tuijin Jishu/Journal of Propulsion Technology, 44(3): 1393–1401. https://doi.org/10.52783/tjjpt.v44.i3.492
Moffo, A.M.F. (2024): A machine learning approach in stress testing US bank holding companies. International Review of Financial Analysis, 95(Part C), 103476. https://doi.org/10.1016/j.irfa.2024.103476
Mogaji, E. – Nguyen, N.P. (2021): Managers’ understanding of artificial intelligence in relation to marketing financial services: Insights from a cross-country study. International Journal of Bank Marketing, 40(6): 1272–1298. https://doi.org/10.1108/ijbm-09-2021-0440
Mostafa, M. (2009): Modeling the efficiency of top Arab banks: A DEA–neural network approach. Expert Systems with Applications, 36(1): 309–320. https://doi.org/10.1016/j.eswa.2007.09.001
Northey, G. – Hunter, V. – Mulcahy, R. – Choong, K. – Mehmet, M. (2022): Man vs machine: How artificial intelligence in banking influences consumer belief in financial advice. International Journal of Bank Marketing, 40(6): 1182–1199. https://doi.org/10.1108/ijbm-09-2021-0439
Norzelan, N.A. – Mohamed, I.S. – Mohamad, M. (2024): Technology acceptance of artificial intelligence (AI) among heads of finance and accounting units in the shared service industry. Technological Forecasting and Social Change, 198, 123022. https://doi.org/10.1016/j.techfore.2023.123022
Omoge, A.P. – Gala, P. – Horky, A. (2022): Disruptive technology and AI in the banking industry of an emerging market. International Journal of Bank Marketing, 40(6): 1217–1247. https://doi.org/10.1108/ijbm-09-2021-0403
Petropoulos, A. – Siakoulis, V. – Stavroulakis, E. – Vlachogiannakis, N.E. (2020): Predicting bank insolvencies using machine learning techniques. International Journal of Forecasting, 36(3): 1092–1113. https://doi.org/10.1016/j.ijforecast.2019.11.005
Prisznyák, A. (2022): Artificial Intelligence in the Banking Sector. Economy and Finance, 9(4): 333–340. https://doi.org/10.33908/EF.2022.4.4
Qian, Y. – Wang, F. – Zhang, M. – Zhong, N. (2024): Political uncertainty, bank loans, and corporate behavior: New investigation with machine learning. Pacific-Basin Finance Journal, 87, 102480. https://doi.org/10.1016/j.pacfin.2024.102480
Rajka, L. – Pollák, Z. (2024): Artificial intelligence for credit risk models, or how do machine learning algorithms compare to traditional models?. Economy and Finance, 11(3): 232–257. https://doi.org/10.33908/EF.2024.3.1
Shahbazi, Z. – Byun, Y.-C. (2022): Machine learning-based analysis of cryptocurrency market financial risk management. IEEE Access, 10: 37848–37856. https://doi.org/10.1109/access.2022.3162858
Singh, P.P. – Anik, F.I. – Senapati, R. – Sinha, A. – Sakib, N. – Hossain, E. (2024): Investigating customer churn in banking: A machine learning approach and visualization app for data science and management. Data Science and Management, 7(1): 7–16. https://doi.org/10.1016/j.dsm.2023.09.002
Sugozu, I.H. – Verberi, C. – Yasar, S. (2025): Machine learning approaches to credit risk: Evaluating Turkish participation and conventional banks. Borsa Istanbul Review, 25(3): 497–512. https://doi.org/10.1016/j.bir.2025.02.001
Sun, S. – Qian, G. – Yu, J. (2024): The impacts of China’s shadow banking regulation on bank lending—An empirical analysis based on textual analysis and machine learning. Pacific-Basin Finance Journal, 88, 102565. https://doi.org/10.1016/j.pacfin.2024.102565
Tang, Y. – Li, H. (2023): Comparing the performance of machine learning methods in predicting soil seed bank persistence. Ecological Informatics, 77, 102188. https://doi.org/10.1016/j.ecoinf.2023.102188
Thi Nguyen, L.Q. – Matousek, R. – Muradoglu, G. (2024): Bank capital, liquidity creation and the moderating role of bank culture: An investigation using a machine learning approach. Journal of Financial Stability, 72, 101265. https://doi.org/10.1016/j.jfs.2024.101265
Uddin, N. – Uddin Ahamed, Md.K. – Uddin, Md.A. – Islam, Md.M. – Talukder, Md.A. – Aryal, S. (2023): An ensemble machine learning-based bank loan approval predictions system with a smart application. International Journal of Cognitive Computing in Engineering, 4: 327–339. https://doi.org/10.1016/j.ijcce.2023.09.001
Wang, L. – Huang, Y. – Hong, Z. (2024): Digitalization as a double-edged sword: A deep learning analysis of risk management in Chinese banks. International Review of Financial Analysis, 94, 103249. https://doi.org/10.1016/j.irfa.2024.103249
Xie, C. – Zhang, J.-L. – Zhu, Y. – Xiong, B. – Wang, G.-J. (2023): How to improve the success of bank telemarketing? Prediction and interpretability analysis based on machine learning. Computers & Industrial Engineering, 175, 108874. https://doi.org/10.1016/j.cie.2022.108874
Zeinalizadeh, N. – Shojaie, A.A. – Shariatmadari, M. (2015): Modeling and analysis of bank customer satisfaction using neural networks approach. International Journal of Bank Marketing, 33(6): 717–732. https://doi.org/10.1108/ijbm-06-2014-0070
Zhou, H. – Sun, G. – Fu, S. – Liu, J. – Zhou, X. – Zhou, J. (2019): A big data mining approach of PSO-based BP neural network for financial risk management with IoT. IEEE Access, 7: 154035–154043. https://doi.org/10.1109/access.2019.2948949
Zsinkó, M. (2025): The Impact of Artificial Intelligence on the Labour Market. Financial and Economic Review, 24(1): 156–170. https://hitelintezetiszemle.mnb.hu/en/fer-24-1-fa1-zsinko