Trustworthy Generative AI for FinTech Risk and Compliance
DOI:
https://doi.org/10.5281/9vbjqp17Keywords:
Trust-Aware Generative AI,Predictive FinTech Analytics,Regulatory Decision Automation,Financial Risk Intelligence,Explainable Artificial Intelligence (XAI),AI-Driven Compliance Monitoring,Generative AI for FinTech,Risk Governance Framework,Automated Regulatory Compliance,Intelligent Financial Decision Support.Abstract
Recent advances in artificial intelligence—particularly large language models—offer substantial promise for automating decision-making across many domains. Yet these generative models also introduce unprecedented risks: because they can express any belief, assumption, or stereotype present in their training data, effective risk control and regulatory oversight become significantly more challenging. This research addresses these challenges in the context of predictive risk assessment for automated decision-making systems in Financial Technology (FinTech), by integrating trust-aware principles into a generative intelligence framework and applying that framework to trust-aware predictive risk assessment.
This approach to trust-aware predictive risk assessment incorporates several key aspects. First, trust is treated as multidimensional, distinguishing between the trustor’s intent and the trustee’s trustworthiness as separate constructs. Second, the work extends beyond developing predictive models evaluated on data governance, quality, and contextual agreement, to also encompass procedural, respect-based, and outcome fairness and robustness—dimensions that remain difficult to assess within existing Trust Science research. Third, trust-aware predictive risk assessments are conceived as potentially interactive rather than strictly unidirectional. Fourth, the resulting data products are designed to explicitly comply with conceptual and regulatory guidelines, including the GDPR and explainability requirements, among others. The framework further supports business decision automation, domain-specific automated analyses, and knowledge-based services, particularly knowledge distillation. Finally, risk assessments are integrated with business decision automation to enable the generation and
evaluation of system trustworthiness across both the design and operational phases.
References
1. Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2020). Explainable AI in Fintech risk management. Frontiers in Artificial Intelligence, 3, Article 26.
2. Yang, Y., Uy, M. C. S., & Huang, A. (2020). FinBERT: A pretrained language model for financial communications. arXiv preprint arXiv:2006.08097.
3. Fritz-Morgenthal, S., Hein, B., & Papenbrock, J. (2022). Financial risk management and explainable, trustworthy, responsible AI. Frontiers in Artificial Intelligence, 5, Article 779799.
4. Selvamuthu, C. M., Yerasuri, S. S., Manukonda, K. R. R., SnvRamanamurthy, B., Kwatra, C. V., & Hemalatha, P. K. (2025, November). IoT Detection and Authentication Using Machine Learning: A Cybersecurity Perspective. In 2025 International Conference on Emerging Engineering Technologies and Applications (IC-EETA) (pp. 417-422). IEEE.
5. Černevičienė, J., & Kabašinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: A systematic literature review. Artificial Intelligence Review, 57(8).
6. Chen, H., Kelly, B., & Xiu, D. (2023). Expected returns and large language models. The Review of Financial Studies.
7. Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., & Mann, G. (2023). BloombergGPT: A large language model for finance. arXiv preprint arXiv:2303.17564.
8. Sekhar, P. C., Karri, M., Vidhya, S. S., Gadi, A. L., Lingineni, S. L., & Perada, A. (2025, September). Intrusion Detection in Smart Grids Using Deep Belief Network and K-Means Clustering Model for Electric Vehicle Charging Systems. In 2025 5th International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) (pp. 1-6). IEEE.
9. Yang, H., Liu, X.-Y., & Wang, C. D. (2023). FinGPT: Open-source financial large language models. arXiv preprint arXiv:2306.06031.
10. Li, Y., Wang, S., Ding, H., & Chen, H. (2023). Large language models in finance: A survey. In Proceedings of the Fourth ACM International Conference on AI in Finance (pp. 374–382).
11. Chen, Z., Ma, J., Zhang, X., Hao, N., Yan, A., Nourbakhsh, A., Yang, X., McAuley, J., Petzold, L. R., & Wang, W. Y. (2024). A survey on large language models for critical societal domains: Finance, healthcare, and law. Transactions on Machine Learning Research.
12. Du, K., Zhao, Y., Mao, R., Xing, F., & Cambria, E. (2025). Natural language processing in finance: A survey. Information Fusion, 115, Article 102755.
13. Yalakaturi, S. N. (2025). AI-DRIVEN DATA OBSERVABILITY FOR RESILIENT DATA CENTER OPERATIONS. International Journal of Applied Mathematics, 38(5s), 1589-1602.
14. Dong, Y., Wu, F., Zhang, K., Dai, Y., Zhang, S., Ye, W., Chen, S., & Cheng, Z.-Q. (2025). Large language model agents in finance: A survey bridging research, practice, and real-world deployment. In Findings of the Association for Computational Linguistics: EMNLP 2025 (pp. 17889–17907).
15. Khan, A. T., Li, S., & Cao, X. (2025). Bridging finance and AI: A comprehensive survey of large language models in financial system. Digital Finance, 7(4), 679–701.
16. Mahadevan, S. (2025). DRIVING SUSTAINABILITY IN AUTO & MANUFACTURING SECTORS: THE ROLE OF CLOUD-BASED SERVERLESS AUTOMATION FOR ERP SYSTEMS. International Journal of Applied Mathematics, 38(7s), 1813-1825.
17. Wang, K., Patel, J., Shen, C., Kim, D., Zhu, A., Lin, A., Borella, L., Osborne, C., White, M., Yang, S., Xiao, K., & Liu, X.-Y. (2025). A report on Financial Regulations Challenge at COLING 2025. In Proceedings of the 31st International Conference on Computational Linguistics.
18. Tatarinov, N., Sukhani, S., Shah, A., & Chava, S. (2025). Language modeling for the future of finance: A quantitative survey into metrics, tasks, and data opportunities. arXiv preprint arXiv:2504.07274.
19. Golec, M., & Alabduljalil, M. (2025). Interpretable LLMs for credit risk: A systematic review and taxonomy. Expert Systems with Applications.
20. National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0). U.S. Department of Commerce.
21. Kolla , S. K. (2025). Generative AI-Enabled Precision Medicine Framework For Longitudinal EHR Intelligence and Personalized Clinical Decision Support. INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, 41-50. https://doi.org/10.29284/kcgr6k25
22. National Institute of Standards and Technology. (2024). Artificial intelligence risk management framework: Generative artificial intelligence profile. U.S. Department of Commerce.
23. Financial Stability Board. (2024). The financial stability implications of artificial intelligence. Financial Stability Board.
24. European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union.
25. Lee, J., Stevens, N., Han, S. C., & Song, M. (2024). A survey of large language models in finance (FinLLMs). arXiv preprint arXiv:2402.02315.
26. Movva, V. R. (2025). GENERATIVE AI-DRIVEN DEVOPS: LEVERAGING LARGE LANGUAGE MODELS FOR INTELLIGENT SOFTWARE DELIVERY AND ENGINEERING PRODUCTIVITY IN RETAIL ENTERPRISES. International Journal of Applied Mathematics, 38(2s), 1621-1631.
27. Zhang, B., Yang, H., & Liu, X.-Y. (2023). Instruct-FinGPT: Financial sentiment analysis by instruction tuning of general-purpose large language models. In Proceedings of the FinLLM Symposium at IJCAI 2023.
28. Zhang, B., Yang, H., Zhou, T., Babar, A., & Liu, X.-Y. (2024). Enhancing financial sentiment analysis via retrieval-augmented large language models. In Proceedings of the ACM International Conference on AI in Finance.
29. Araci, D. (2019). FinBERT: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.
30. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (Vol. 30).
31. Reddy Manukonda, K. R. (2023). Investigating the role of exploratory testing in agile software development: a case study analysis. Journal of Artificial Intelligence & Cloud Computing, 2(4), 1-5.
32. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144).
33. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229).
34. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., III, & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92.
35. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
36. Bandi, V. D. V. K. AI-Based Anomaly Detection Frameworks in Distributed Enterprise Data Systems.
37. Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, A., Balle, B., Kasirzadeh, A., et al. (2022). Taxonomy of risks posed by language models. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 214–229).
38. Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), Article 248.
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