ARTIFICIAL INTELLIGENCE IN FINANCIAL RISK MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW ON ENHANCING ORGANIZATIONAL RESILIENCE FOR FUTURE GLOBAL FINANCIAL CRISES

Yonghwa Han; Andini Nurwulandari; Hasanudin; Aghnia Wulandari

doi:10.62567/micjo.v3i1.1572

Authors

Yonghwa Han National University
Andini Nurwulandari National University
Hasanudin National University
Aghnia Wulandari National University

DOI:

https://doi.org/10.62567/micjo.v3i1.1572

Keywords:

Artificial Intelligence, Financial Risk Management, Global Financial Crises, Machine Learning, Organizational Resilience

Abstract

This study explores how incorporating artificial intelligence improves institutional resilience and overcomes the rigidity of conventional, data-based methods to alter financial risk management. To find patterns in AI applications, resilience theory, and integration pathways, a qualitative systematic literature review was carried out utilizing theme synthesis in accordance with PRISMA peer-reviewed protocols. Findings show that AI techniques, machine learning for tail-risk detection, deep learning for high-frequency forecasting, and explainable AI for transparent decisions, yield up to 28% reductions in forecasting errors and halve recovery times during crises. The hybrid CNN Transformer architectures and transformer-based NLP models significantly enhance predictive accuracy and forward-looking insights. The study suggests financial institutions adopt integrated AI frameworks, invest in data quality and human–AI collaboration, and implement principle-based governance to balance innovation with fairness and stability. Limitations include reliance on published literature and limited representation of emerging AI models, warranting future longitudinal and context-specific empirical research.

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References

Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564–608.

https://doi.org/10.1257/aer.20130456.

Akinjole, A., Shobayo, O., Popoola, J., Okoyeigbo, O., & Ogunleye, B. (2024). Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction. Mathematics, 12(21), 3423.

https://doi.org/10.3390/math12213423.

Alvi, J., & Arif, I. (2024). Advancing financial resilience: A systematic review of default prediction models and future directions in credit risk management. Heliyon, 10(21), e39770. https://doi.org/10.1016/j.heliyon.2024.e39770.

Anang, A.N. et al. (2024). Explainable AI in financial technologies: Balancing innovation with regulatory compliance. International Journal of Science and Research Archive, 13(1), pp. 1793–1806. https://doi.org/10.30574/ijsra.2024.13.1.1870.

Andrae, S. (2024). Explainable Artificial Intelligence in Risk Management: A Framework. Transformations in Banking, Finance and Regulation, pp. 149–206.

https://doi.org/10.1142/9781800615212_0004.

Berg, T., Burg, V., Gombović, A., & Puri, M. (2020). On the rise of FinTechs: Credit scoring using digital footprints. The Review of Financial Studies, 33(7), 2845–2897. https://doi.org/10.1093/rfs/hhz099.

Bitetto, A. et al. (2023). Machine learning and credit risk: Empirical evidence from small and mid-sized businesses. Socio-Economic Planning Sciences, 90, 101764. https://doi.org/10.1016/j.seps.2023.101746.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa.

Camilleri, M. A. (2024). Artificial intelligence governance: Ethical considerations and regulatory structures. Expert Systems with Applications, 238, 121899.

https://doi.org/10.1111/exsy.13406.

Caccioli, F., Barucca, P. and Kobayashi, T. (2018) 'Network models of financial systemic risk: a review,' Journal of Computational Social Science, 1(1), pp. 81–114.

https://doi.org/10.1007/s42001-017-0008-3.

Chen, Z., Li, H., Wang, T., Wu, J. (2023). How digital transformation affects bank risk: Evidence from listed Chinese banks. Finance Research Letters, 58, p. 104319.

https://doi.org/10.1016/j.frl.2023.104319.

Damayanti, I. and Suryani, I.P. (2024) 'Supplementary Strategies for Organizational Resilience in the Times of Crises: A Literature review, 1(2), pp. 70–78.

https://doi.org/10.31603/itej.12063.

DesJardine, M., Bansal, P., & Yang, Y. (2019). Bouncing back: Building resilience through social and environmental practices in the context of the 2008 global financial crisis. Journal of Management, 45(4), 1434–1460.

https://doi.org/10.1177/0149206317708854.

Du, K. et al. (2024). Natural language processing in finance: A survey. Information Fusion, 115, p. 102755. https://doi.org/10.1016/j.inffus.2024.102755.

Durairaj, M., & Mohan, B. G. K. (2022). A convolutional neural network-based approach to financial time series prediction. Neural Computing and Applications, 34(16), 13319–13337. https://doi.org/10.1007/s00521-022-06922-9.

Gambacorta, L., Huang, Y., Qiu, H., & Wang, J. (2019). How Do Machine Learning and Non-Traditional Data Affect Credit Scoring? New Evidence from a Chinese Fintech Firm. Risk Management eJournal. https://doi.org/10.1016/j.jfs.2024.101284.

Giantsidi, S., & Claudia, T. (2025). Deep learning for financial forecasting: A review of recent advancements. Computational Economics, 65(4), 1123–1168.

https://doi.org/10.1007/s10614-024-10563-2.

Gibson C. A., Tarrant M. (2010). A ’conceptual models’ approach to organisational resilience. Australian Journal of Emergency Management 25(2): 6–12.

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009.

Halbusi, H.A. et al. (2025). The nexus of managerial and technical AI knoweldge, disruptive innovation and the circular economy: The role of organizational change capability and financial resilience. Technology in Society, p. 102937.

https://doi.org/10.1016/j.techsoc.2025.102937.

Heß, V. L., & Damásio, B. (2025). Machine learning in banking risk management: Mapping a decade of evolution. Annals of Operations Research, 334(1-3), 1–28.

https://doi.org/10.1016/j.jjimei.2025.100324.

Juliana, B.M. et al. (2023). The analysis effect of adaptive leadership, digital adoption, and organization agility on the resilience of Indonesian banks. Proceeding Medan International Conference on Economic and Business. https://doi.org/10.30596/miceb.v1i0.300.

Karanikola A, Davrazos G, Liapis CM, Kotsiantis S. Financial sentiment analysis: Classic methods vs. deep learning models. Intelligent Decision Technologies.

https://doi.org/10.3233/IDT-230478.

Kothandapani, N.H.P. (2024). Automating financial compliance with AI: A New Era in regulatory technology (RegTech). International Journal of Science and Research Archive, 11(1), pp. 2646–2659. https://doi.org/10.30574/ijsra.2024.11.1.0040.

Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors. Technological and Economic Development of Economy, 25(5), 716-742. https://doi.org/10.3846/tede.2019.8740.

Li, B. and Zhang, X. (2024). Systemic risk and financial networks. The Quarterly Review of Economics and Finance, 94, pp. 25–36. https://doi.org/10.1016/j.qref.2023.12.012.

Li, H. (2025). Hybrid Multilayer Perceptron-Adam Optimization based Early Warning System for Financial Crisis Prediction. IEEE Xplore.

https://doi.org/10.1109/icdsis65355.2025.11070600.

Lin, W. Y., Hu, Y. H., and Tsai, C.F. (2011). Machine Learning in Financial Crisis Prediction: A Survey. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 42(4), pp. 421–436.

https://doi.org/10.1109/tsmcc.2011.2170420.

McCarthy, J. (2023). Cyber-risks in modern finance: Building operational and digital resilience. Journal of International Banking Law and Regulation, 38(7), 233–248.

https://doi.org/10.2139/ssrn.4881572.

Mengist, W., Soromessa, T., & Legese, G. (2020). Method for conducting systematic literature review and meta-analysis for environmental science research. MethodsX, 7, 100777. https://doi.org/10.1016/j.mex.2019.100777.

Mestiri, S. (2024). Credit scoring using machine learning and deep learning-based models. Data Science in Finance and Economics, 4(2), 236–248. https://doi.org/10.3934/DSFE.2024009.

Mohsin, M. T. & Nasim, N. B., & Ahmed, S. (2025). Explaining the unexplainable: A systematic review of explainable AI in finance. International Journal of Science and Research Archive, 16(03), 476–497. https://doi.org/10.30574/ijsra.2025.16.3.2581.

Mokheleli, T., & Museba, T. (2023). Machine Learning Approach for Credit Score Predictions. Journal of Information Systems and Informatics, 5(2), 497-517.

https://doi.org/10.51519/journalisi.v5i2.487.

Olawale, N.O. et al. (2024). RegTech innovations streamlining compliance, reducing costs in the financial sector. GSC Advanced Research and Reviews, 19(1), pp. 114–131. https://doi.org/10.30574/gscarr.2024.19.1.0146.

Ortiz-de-Mandojana, N., & Bansal, P. (2016). The long-term benefits of organizational resilience through sustainable business practices. Strategic Management Journal, 37(8), 1615–1631. https://doi.org/10.1002/smj.2410.

Page, M. J., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. The BMJ, 372, n71. https://doi.org/10.1136/bmj.n71.

Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2024). Responsible artificial intelligence governance: A review of regulatory frameworks and organizational practices. Information Systems Frontiers, 27(1), 245–272. https://doi.org/10.1007/s10796-024-10498-2.

Simón, C., Ferreras-Méndez, J. L., Neumann, W. P., & Arias-Aranda, D. (2024). Integrating AI in organizations for value creation through human–AI interactions: A dynamic capabilities-based framework. International Journal of Information Management, 79, 102839. https://doi.org/10.1016/j.jbusres.2024.114783.

Stulz, R. M. (2023). Crisis risk and risk management. European Financial Management, 29(4), 1097–1126. https://doi.org/10.1111/eufm.12441.

Sudra, R. (2024). Regulatory compliance with AI and risks involved in finance and banking sectors. Journal of Scientific and Engineering Research, 11(1), 276–285. https://doi.org/10.5281/zenodo.12772054.

Tan, B. & Lin, Y. (2023). Early Warning of Companies' Credit Risk Based on Machine Learning. International Journal of Information Technologies and Systems Approach (IJITSA), 16(3), 1-21. https://doi.org/10.4018/IJITSA.324067.

Theodorakopoulos, L., Theodoropoulou, A., & Bakalis, A. (2025). Big data in financial risk management: Evidence, advances, and open questions: A systematic review. Frontiers in Artificial Intelligence, 8, 1658375. https://doi.org/10.3389/frai.2025.1658375.

Tian, X., Tian, Z., Khatib, S. F. A., & Wang, Y. (2024). Machine learning in internet financial risk management: A systematic literature review. PLOS ONE, 19(4), e0300195. https://doi.org/10.1371/journal.pone.0300195.

Vashishth, T. K., Chaudhary, A., Sharma, V., Chaudhary, S., Sharma, N., Sharma, R., Kaushik, V., & Sharma, S. (2025). Adaptive AI Systems for Financial Fraud Detection and Risk Management. In A. Derbali (Ed.), Artificial Intelligence for Financial Risk Management and Analysis (pp. 431-454). IGI Global Scientific Publishing.

https://doi.org/10.4018/979-8-3373-1200-2.ch021.

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), 84. https://doi.org/10.1057/s41599-025-04850-8.

Wang, M. et al. (2025). Explainable Machine learning in Risk Management: Balancing accuracy and interpretability. Journal of Financial Risk Management, 14(03), pp. 185–198.

https://doi.org/10.4236/jfrm.2025.143011.

Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., & Veloso, M. (2023). Financial time series forecasting using CNN and Transformer. Proceedings of the AAAI Conference on Artificial Intelligence - AI for Financial Services Bridge Program, 37, 15897–15903. https://doi.org/10.48550/arXiv.2304.04912.