Large Language Models in Digital Banking: A Systematic Review and Implications for Financial Inclusion (2015–2025)
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Abstract
The rapid emergence of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) is fundamentally transforming the financial sector by enabling advanced capabilities in processing large volumes of unstructured textual data. While previous studies have explored broad applications of artificial intelligence in finance, a dedicated synthesis focusing specifically on banking management and its implications for digital banking transformation remains limited. In response, this article provides a systematic state-of-the-art review of 91 studies published between 2015 and 2025, identified through the Web of Science and Scopus databases using a PRISMA-based methodology.The review synthesizes the literature across five key domains: (1) Policy Interpretation and Sentiment Analysis, (2) Risk Management and Financial Prediction, (3) Regulatory Technology (RegTech) and Compliance, (4) Operational Efficiency and Process Management, and (5) Customer-Facing Applications and Advisory Services. Bibliometric evidence reveals a rapid acceleration of scholarly interest beginning in 2023, with Risk Management and Financial Prediction representing the most prominent research stream (28.7% of publications). The findings demonstrate how LLM-driven tools are redefining traditional banking management practices by enabling contextual interpretation of central bank communications, improving financial risk forecasting, automating regulatory compliance processes, and enhancing operational decision-making. digital banking services in improving customer interaction, expanding access to financial information, and potentially supporting broader financial inclusion within increasingly digitalized financial systems. Finally, the study identifies critical limitations in the existing literature particularly regarding data privacy, model interpretability, and geographic bias and proposes a strategic roadmap outlining future research directions for responsible and inclusive AI adoption in banking.
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References
- Aarab, I. (2025). LLM-based IR-system for bank supervisors. Knowledge-Based Systems, 310. https://doi.org/10.1016/j.knosys.2024.112914
- Aldasoro, I., Gambacorta, L., Korinek, A., Shreeti, V., & Stein, M. (2025). Intelligent financial system: How AI is transforming finance. Journal of Financial Stability, 81. https://doi.org/10.1016/j.jfs.2025.101472
- Ali, H., Zafar, M. B., & Aysan, A. F. (2025). Generative AI in finance: Replicability, methodological contingencies, and future research directions. Finance Research Letters, 86(F). https://doi.org/10.1016/j.frl.2025.108797
- Alonso-Robisco, A., & Carbó, J. M. (2023). Analysis of CBDC narrative by central banks using large language models. Finance Research Letters, 58(C). https://doi.org/10.1016/j.frl.2023.104643
- Alonso, S. L. N., Ozili, P. K., Hernández, B. M. S., & Pacheco, L. M. (2025). Evaluating the acceptance of CBDCs: experimental research with artificial intelligence (AI) generated synthetic response. Quantitative Finance and Economics, 9(1), 242–273. https://doi.org/10.3934/QFE.2025008
- Ang, Y., Bao, Y., Jiang, L., Tao, J., Tung, A. K. H., Szpurch, L., & Ni, H. (2025). Structured Agentic Workflows for Financial Time-Series Modelling with LLMs and Reflective Feedback. ICAIF 2025 - 6th ACM International Conference on AI in Finance, 924–932. https://doi.org/10.1145/3768292.3771251
- Ardekani, A. M., Bertz, J., Bryce, C., Dowling, M., & Long, S. (2024). FinSentGPT: A universal financial sentiment engine? International Review of Financial Analysis, 94. https://doi.org/10.1016/j.irfa.2024.103291
- Bae, J., Yu Hung, C., & van Lent, L. (2023). Mobilizing Text As Data. European Accounting Review, 32(5), 1085–1106. https://doi.org/10.1080/09638180.2023.2218423
- Baerg, N., & Binder, C. (2024). Automated Detection of Emotion in Central Bank Communication: a Warning. National Institute Economic Review, 269(SI), 82–91. https://doi.org/10.1017/nie.2024.31
- Beckmann, L., & Hark, P. F. (2024). ChatGPT and the banking business: Insights from the US stock market on potential implications for banks. Finance Research Letters, 63. https://doi.org/10.1016/j.frl.2024.105237
- Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., Ca, J. U., Kandola, J., Hofmann, T., Poggio, T., & Shawe-Taylor, J. (2003). A Neural Probabilistic Language Model. Journal of Machine Learning Research, 3, 1137–1155.
- Benjamens, S., Dhunnoo, P., & Meskó, B. (2020). The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. Npj Digital Medicine 2020 3:1, 3(1), 1–8. https://doi.org/10.1038/s41746-020-00324-0
- Bergener, P., Delfmann, P., Weiss, B., & Winkelmann, A. (2015). Detecting potential weaknesses in business processes: An exploration of semantic pattern matching in process models. Business Process Management Journal, 21(1), 25–54. https://doi.org/10.1108/BPMJ-07-2013-0103
- Bertsch, C., Hull, I., Lumsdaine, R. L., & Zhang, X. (2025). Central bank mandates and monetary policy stances: Through the lens of Federal Reserve speeches. Journal of Econometrics, 249(A). https://doi.org/10.1016/j.jeconom.2025.105948
- Borchert, P., Coussement, K., De Caigny, A., & De Weerdt, J. (2023). Extending business failure prediction models with textual website content using deep learning. European Journal of Operational Research, 306(1), 348–357. https://doi.org/10.1016/j.ejor.2022.06.060
- Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 2020-December. https://arxiv.org/abs/2005.14165v4
- Buehler, S., Kaiser, C., & Jaeger, F. (2012). The geographic determinants of bankruptcy: Evidence from Switzerland. Small Business Economics, 39(1), 231–251. https://doi.org/10.1007/s11187-010-9301-8
- Cao, Z., & Feinstein, Z. (2024). Large Language Model in Financial Regulatory Interpretation. 2024 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, CIFEr 2024. https://doi.org/10.1109/CIFER62890.2024.10772991
- Cho, D., & Jung, J. (2026). Mind the tone: Responses of inflation expectations to central bankers’ speeches. Journal of International Money and Finance, 160. https://doi.org/10.1016/j.jimonfin.2025.103452
- Chuffart, T., & Dell’Eva, C. (2024). Media Coverage of the ECB: a Textual Analysis. Revue d’économie Politique, Vol. 134(6), 923–945. https://doi.org/10.3917/redp.346.0923
- Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tai, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., Webson, A., Gu, S. S., Dai, Z., Suzgun, M., Chen, X., Chowdhery, A., Castro-Ros, A., Pellat, M., Robinson, K., … Wei, J. (2022). Scaling Instruction-Finetuned Language Models. Journal of Machine Learning Research, 25. https://arxiv.org/pdf/2210.11416
- Coelho E Silva, L., Fonseca, G. D. F., & Castro, P. A. L. (2024). Transformers and attention-based networks in quantitative trading: a comprehensive survey. ICAIF 2024 - 5th ACM International Conference on AI in Finance, 822–830. https://doi.org/10.1145/3677052.3698684
- Damiano, R., Polizzi, S., Scannella, E., & Valenza, G. (2025). Corruption Detection Through Textual Analysis: Evidence From Eurozone Banks. Business Ethics, the Environment and Responsibility. https://doi.org/10.1111/beer.12824
- Davies, R., & Hellings, J. (2024). Britain’S Trade Challenge: Tracking the Costs in Real Time. National Institute Economic Review, 268, 15–29. https://doi.org/10.1017/nie.2024.15
- Devlin, J., Chang, M.-W., Lee, K., Google, K. T., & Language, A. I. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Naacl-Hlt 2019, Mlm, 4171–4186. https://aclanthology.org/N19-1423.pdf
- Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1, 4171–4186. https://arxiv.org/abs/1810.04805v2
- Dong, M. M., Stratopoulos, T. C., & Wang, V. X. (2024). A scoping review of ChatGPT research in accounting and finance. International Journal of Accounting Information Systems, 55. https://doi.org/10.1016/j.accinf.2024.100715
- Dündar, E. B., Kiliç, O. F., Çekiç, T., Manav, Y., & Deniz, O. (2020). Large scale intent detection in turkish short sentences with contextual word embeddings. IC3K 2020 - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 1, 187–192. https://doi.org/10.5220/0010108301870192
- Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71. https://doi.org/10.1016/j.ijinfomgt.2023.102642
- Eisfeldt, A. L., & Schubert, G. (2025). Generative AI and Finance. Annual Review of Financial Economics, 17(1), 363–393. https://doi.org/10.1146/annurev-financial-112923-020503
- Elsevier. (2020). Mendeley Reference Manager (v1.19.8). https://www.mendeley.com/download-reference-manager/windows
- Fan, M. (2024). LLMs in Banking: Applications Challenges and Approaches. Proceedings of International Conference on Digital Economy, Blockchain and Artificial Intelligence, DEBAI 2024, 314–321. https://doi.org/10.1145/3700058.3700107
- Feng, Z., Hu, G., Li, B., & Wang, J. (2025). Unleashing the power of ChatGPT in finance research: opportunities and challenges. Financial Innovation, 11(1). https://doi.org/10.1186/s40854-025-00770-3
- Fraccaroli, N., Arel-Bundock, V., & Blyth, M. (2025). What do central bankers talk about when they talk about inflation? The rise and fall of inflation narratives. New Political Economy, 30(5), 713–728. https://doi.org/10.1080/13563467.2025.2504392
- Gupta, A., Lu, C., Simaan, M., & Zaki, M. J. (2025). When Positive Sentiment is not so Positive: Textual Analytics and Bank Failures. Computational Economics. https://doi.org/10.1007/s10614-025-10969-2
- Haidar, A., & Abbass, A. (2025). Navigating the Frontier of Finance: A Scoping Review of Generative AI Applications and Implications. Generative Artificial Intelligence in Finance: Large Language Models, Interfaces, and Industry Use Cases to Transform Accounting and Finance Processes, 215–252. https://doi.org/10.1002/9781394271078.ch12
- Hens, T., & Nordlie, T. (2025). How good are LLMs in risk profiling? Finance Research Letters, 85(D). https://doi.org/10.1016/j.frl.2025.108102
- Huong, H., Nguyen, X., Dang, T. K., & Tran-Truong, P. T. (2024). Money Laundering Detection Using A Transaction-Based Graph Learning Approach. Proceedings of the 2024 18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024. https://doi.org/10.1109/IMCOM60618.2024.10418307
- Iadisernia, G., & Camassa, C. (2025). Prompting for Policy: Forecasting Macroeconomic Scenarios with Synthetic LLM Personas. ICAIF 2025 - 6th ACM International Conference on AI in Finance, 335–343. https://doi.org/10.1145/3768292.3770385
- Ito, A., Sato, M., & Ota, R. (2025). A novel content-based approach to measuring monetary policy uncertainty using fine-tuned LLMs. Finance Research Letters, 75. https://doi.org/10.1016/j.frl.2025.106832
- Jelinek, F. (1976). Continuous Speech Recognition by Statistical Methods. Proceedings of the IEEE, 64(4), 532–556. https://doi.org/10.1109/PROC.1976.10159
- Kanelis, D., & Siklos, P. L. (2025). The ECB press conference statement: deriving a new sentiment indicator for the euro area. International Journal of Finance and Economics, 30(1), 652–664. https://doi.org/10.1002/ijfe.2940
- Kim, H., Yoo, Y., & Kwak, Y. (2025). Query Generation Pipeline with Enhanced Answerability Assessment for Financial Information Retrieval. ICAIF 2025 - 6th ACM International Conference on AI in Finance, 141–149. https://doi.org/10.1145/3768292.3770354
- Kim, S., & Ryu, M. H. (2025). Online review based IPA and IPCA: the case of Korean mobile banking apps. International Journal of Bank Marketing, 43(4), 731–756. https://doi.org/10.1108/IJBM-03-2024-0136
- Kim, W., Spörer, J., Lee, C. L., & Handschuh, S. (2024). Is Small Really Beautiful for Central Bank Communication? Evaluating Language Models for Finance: Llama-3-70B, GPT-4, FinBERT-FOMC, FinBERT, and VADER. ICAIF 2024 - 5th ACM International Conference on AI in Finance, 626–633. https://doi.org/10.1145/3677052.3698675
- Ko, E., Dmonte, A., & Zampieri, M. (2025). Comparison of an affine term structure model with Fed chair speeches in large language models. Finance Research Letters, 78. https://doi.org/10.1016/j.frl.2025.107114
- Kong, Y., Nie, Y., Dong, X., Mulvey, J. M., Poor, H. V., Wen, Q., & Zohren, S. (2024). Large Language Models for Financial and Investment Management: Applications and Benchmarks. Journal of Portfolio Management, 51(2), 162–210. https://doi.org/10.3905/jpm.2024.1.645
- Korangi, K., Mues, C., & Bravo, C. (2023). A transformer-based model for default prediction in mid-cap corporate markets. European Journal of Operational Research, 308(1), 306–320. https://doi.org/10.1016/j.ejor.2022.10.032
- Lakkaraju, K., Jones, S. E., Vuruma, S. K. R., Pallagani, V., Muppasani, B. C., & Srivastava, B. (2023). LLMs for Financial Advisement: A Fairness and Efficacy Study in Personal Decision Making. ICAIF 2023 - 4th ACM International Conference on AI in Finance, 100–107. https://doi.org/10.1145/3604237.3626867
- Lee, D. K. C., Guan, C., Yu, Y., & Ding, Q. (2024). A Comprehensive Review of Generative AI in Finance. FinTech, 3(3), 460–478. https://doi.org/10.3390/fintech3030025
- Leek, L., & Bischl, S. (2025). How central bank independence shapes monetary policy communication: A Large Language Model application. European Journal of Political Economy, 87. https://doi.org/10.1016/j.ejpoleco.2025.102668
- Lengyel, P., Pancsira, J., & Füzesi, I. (2025). Machine Learning Integration in Cryptocurrency Trading: A Systematic Review of Fintech Implications. https://doi.org/10.21203/RS.3.RS-7411123/V1
- Li, Y., Wang, S., Ding, H., & Chen, H. (2023). Large Language Models in Finance: A Survey. ICAIF 2023 - 4th ACM International Conference on AI in Finance, 374–382. https://doi.org/10.1145/3604237.3626869
- Lin, J., Lai, S., Yu, H., Liang, R., & Yen, J. (2025). ChatGPT based credit rating and default forecasting. Journal of Data, Information and Management, 7(1), 69–92. https://doi.org/10.1007/s42488-025-00143-6
- Lis, S., Kubkowski, M., Borkowska, O., Serwa, D., & Kurpanik, J. (2024). Analyzing credit risk model problems through natural language processing-based clustering and machine learning: insights from validation reports. Journal of Risk Model Validation, 18(2), 59–86. https://doi.org/10.21314/JRMV.2024.006
- Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V., & Allen, P. G. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. https://arxiv.org/pdf/1907.11692
- Loukas, L., Stogiannidis, I., Diamantopoulos, O., Malakasiotis, P., & Vassos, S. (2023). Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking. ICAIF 2023 - 4th ACM International Conference on AI in Finance, 392–400. https://doi.org/10.1145/3604237.3626891
- Lu, H., & Wang, X. (2025). Stranded asset risk and corporate capital structure: Evidence from China’s low-carbon transition. Research in International Business and Finance, 80. https://doi.org/10.1016/j.ribaf.2025.103144
- Mahendran, M. B., Gokul, A. K., Lakshmi, P., & Pavithra, S. (2025). Comparative Advances in Financial Sentiment Analysis:A Review of BERT,FinBert, and Large Language Models. 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025, 39–45. https://doi.org/10.1109/IDCIOT64235.2025.10914764
- Martin, L., Muller, B., Suárez, P. J. O., Dupont, Y., Romary, L., de la Clergerie, É. V., Seddah, D., & Sagot, B. (2020). CamemBERT: a Tasty French Language Model. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 7203–7219. https://doi.org/10.18653/v1/2020.acl-main.645
- Mohamed, Maha Mohamed Alsebai, Mohamed Djafar Henni, and Nema Amin Alsayed Sorour. 2026. 'Integrating Digital and AI-Driven Productivity into National Accounts: A Systemic Analysis of Economic Impacts in Emerging and Advanced Economies', Sustainability, 18: 878.
- Metzger, M., O’Reilly, S., & Mac an Bhaird, C. (2025). Generative artificial intelligence augmenting SME financial management. Technovation, 147. https://doi.org/10.1016/j.technovation.2025.103313
- Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings. https://arxiv.org/abs/1301.3781v3
- Mo, H., & Ouyang, S. (2025). (Generative) AI in Financial Economics. Journal of Chinese Economic and Business Studies, 23(4), 509–587. https://doi.org/10.1080/14765284.2025.2569006
- Moayeri, M., Tabassi, E., & Feizi, S. (2024). WorldBench: Quantifying Geographic Disparities in LLM Factual Recall. 2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2024, 1211–1228. https://doi.org/10.1145/3630106.3658967
- Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal of Surgery, 8(5), 336–341. https://doi.org/10.1016/J.IJSU.2010.02.007
- Moreno, A. I., & Caminero, T. (2024). Assessing climate-related disclosures of European banks through text mining. Review of World Economics. https://doi.org/10.1007/s10290-024-00531-x
- Muggleton, S. (2014). Alan turing and the development of artificial intelligence. AI Communications, 27(1), 3–10. https://doi.org/10.3233/AIC-130579
- OpenAI. (2024). ChatGPT. [Large Language Model]. https://chat.openai.com/chat
- Ospina-Tejeiro, J. J., & Romero, J. V. (2025). Monetary policy transparency in Colombia. Latin American Journal of Central Banking. https://doi.org/10.1016/j.latcb.2025.100185
- Patterson, A. (2025). Assessing the Alignment of FOMC Statements with Minutes using Large Language Models. Issues in Information Systems, 26(2), 96–108. https://doi.org/10.48009/2_iis_108
- Peng, X., Han, C., Ouyang, F., & Liu, Z. (2020). Topic tracking model for analyzing student-generated posts in SPOC discussion forums. International Journal of Educational Technology in Higher Education, 17(1), 1–22. https://doi.org/10.1186/S41239-020-00211-4/TABLES/6
- Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 1532–1543. https://doi.org/10.3115/V1/D14-1162
- Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1, 2227–2237. https://doi.org/10.18653/v1/n18-1202
- Pfeifer, M., & Marohl, V. P. (2023). CentralBankRoBERTa: A fine-tuned large language model for central bank communications. Journal of Finance and Data Science, 9. https://doi.org/10.1016/j.jfds.2023.100114
- Prakash, P., Shreeya, Y. C., Srivastava, A., Chaitanya Priya, K. J., & Sharma, A. (2021). A Conceptual Model Simulation to Detect and Report City Traffic Violations using Distributed Intelligent Agents. 2021 6th International Conference for Convergence in Technology, I2CT 2021. https://doi.org/10.1109/I2CT51068.2021.9417881
- Radford, A., & Narasimhan, K. (2018). Improving Language Understanding by Generative Pre-Training.
- Raliphada, P., Olusanya, M., & Olukanmi, S. (2025). Transformer-based NLP Approaches for Credit Risk Prediction: A Systematic Review. https://www.researchsquare.com/article/rs-7707021/v1
- Roychoudhury, S., Sunkle, S., Choudhary, N., Kholkar, D., & Kulkarni, V. (2018). A case study on modeling and validating financial regulations using (semi-) automated compliance framework. Lecture Notes in Business Information Processing, 335, 288–302. https://doi.org/10.1007/978-3-030-02302-7_18
- Rybinski, K. (2021). Ranking professional forecasters by the predictive power of their narratives. International Journal of Forecasting, 37(1), 186–204. https://doi.org/10.1016/j.ijforecast.2020.04.003
- Saleh, L., & Semaan, S. (2024). The Potential of AI in Performing Financial Sentiment Analysis for Predicting Entrepreneur Survival. Administrative Sciences, 14(9). https://doi.org/10.3390/admsci14090220
- Saxena, A., Verma, S., & Mahajan, J. (2024). Generative AI in banking financial services and insurance: A guide to use cases, approaches, and insights. Generative AI in Banking Financial Services and Insurance: A Guide to Use Cases, Approaches, and Insights, 1–346. https://doi.org/10.1007/979-8-8688-0559-2
- Sayed, A., Kanojia, M., & Nabajja, S. (2024). Advanced Subjective Question Bank Generation Using Retrieval Augmented Generation Architecture. International Journal of Computer Information Systems and Industrial Management Applications, 16(3), 264–277. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201672275&partnerID=40&md5=03ece3f3ff81a9dcd748794841f45b01
- Sideras, A., Bougiatiotis, K., Zavitsanos, E., Paliouras, G., & Vouros, G. (2024). Bankruptcy Prediction: Data Augmentation, LLMs and the Need for Auditor’s Opinion. ICAIF 2024 - 5th ACM International Conference on AI in Finance, 453–460. https://doi.org/10.1145/3677052.3698627
- Simionescu, M., & Nicula, A.-S. (2024). Sentiment Analysis as an Innovation in Inflation Forecasting in Romania. Marketing and Management of Innovations, 15(2), 13–25. https://doi.org/10.21272/mmi.2024.2-02
- Skalski, P., Sutton, D., Burrell, S., Perez, I., & Wong, J. (2023). Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences. ICAIF 2023 - 4th ACM International Conference on AI in Finance, 141–149. https://doi.org/10.1145/3604237.3626850
- Soedarmono, W., Pramono, S. E., & Tarazi, A. (2017). The procyclicality of loan loss provisions in Islamic banks. Research in International Business and Finance, 39(B), 911–919. https://doi.org/10.1016/j.ribaf.2016.05.003
- Srivastava, V. (2024). Lending an Ear: How LLMs Hear Your Banking Intentions. ICAIF 2024 - 5th ACM International Conference on AI in Finance, 301–309. https://doi.org/10.1145/3677052.3698608
- Stander, Y. S. (2024). A News Sentiment Index to Inform International Financial Reporting Standard 9 Impairments. Journal of Risk and Financial Management, 17(7). https://doi.org/10.3390/jrfm17070282
- Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. Advances in Neural Information Processing Systems, 4(January), 3104–3112. https://arxiv.org/abs/1409.3215v3
- Sychev, O., & Shashkov, D. (2025). Mass Generation of Programming Learning Problems from Public Code Repositories. Big Data and Cognitive Computing, 9(3). https://doi.org/10.3390/bdcc9030057
- Tadesse, B., & White, R. (2016). Do immigrants affect the profile of U.S. exporters? Applied Economics, 48(19), 1743–1758. https://doi.org/10.1080/00036846.2015.1105930
- Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., & Lample, G. (2023). LLaMA: Open and Efficient Foundation Language Models. https://arxiv.org/pdf/2302.13971
- Tsionas, E. G., & Mamatzakis, E. C. (2017). Adjustment costs in the technical efficiency: An application to global banking. European Journal of Operational Research, 256(2), 640–649. https://doi.org/10.1016/j.ejor.2016.06.037
- Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 435–460. https://doi.org/10.1007/978-1-4020-6710-5_3
- van Eck, N. J., & Waltman, L. (2009). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2009 84:2, 84(2), 523–538. https://doi.org/10.1007/S11192-009-0146-3
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 2017-December, 5999–6009. https://arxiv.org/abs/1706.03762v7
- Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2018). GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. EMNLP 2018 - 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, Proceedings of the 1st Workshop, 353–355. https://doi.org/10.18653/v1/w18-5446
- Wang, J., Liu, G., Cheng, Y., Xu, X., & Li, Z. (2025). Leveraging Internet-Sourced Text Data for Financial Analytics in Supply Chain Finance: A Large Language Model-Enhanced Text Mining Workflow. IEEE Transactions on Engineering Management, 72, 1924–1938. https://doi.org/10.1109/TEM.2025.3567302
- Wang, K. (Wei), & Zhong, C. (Xiaoqian). (2025). The dark side of innovation policy uncertainty. Finance Research Letters, 85. https://doi.org/10.1016/j.frl.2025.108066
- Wang, Y., Li, H., & Liao, L. (2020). DEM Construction Method for Slopes Using Three-Dimensional Point Cloud Data Based on Moving Least Square Theory. JOURNAL OF SURVEYING ENGINEERING, 146(3). https://doi.org/10.1061/(ASCE)SU.1943-5428.0000320
- Weizenbaum, J. (1966). ELIZA-A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. https://doi.org/10.1145/365153.365168/ASSET/52A33E60-61E1-440C-8557-F51EDCC5D9BF/ASSETS/365153.365168.FP.PNG
- Wu, Z., Dong, Y., Li, Y., & Shi, B. (2025). Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts. European Journal of Operational Research, 326(3), 691–706. https://doi.org/10.1016/j.ejor.2025.04.032
- Xi, Y., Liu, W., Lin, J., Chen, B., Tang, R., Zhang, W., & Yu, Y. (2024). MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models. International Conference on Information and Knowledge Management, Proceedings, 2585–2595. https://doi.org/10.1145/3627673.3679599
- Yim, T. Y., Tan, W., Zhang, Y., Lam, T. W., & Yiu, S. M. (2025). Demystifying TCFD Disclosures: An AI-Powered Framework for Enhanced Transparency and Trust. ICAIF 2025 - 6th ACM International Conference on AI in Finance, 578–586. https://doi.org/10.1145/3768292.3770400
- Zhang, J., Li, Y., Liu, Y. H., & Xie, L. (2024). Research and Practice of NL2SQL Technology Based on LLM for Big Data of Enterprise Finance. Proceedings - 2024 4th International Conference on Advanced Enterprise Information System, AEIS 2024, 46–51. https://doi.org/10.1109/AEIS65978.2024.00015