When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech

Computer Science > Cryptography and Security [Submitted on 28 May 2026] When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech Qingwen Zeng, Zhenghao Zhao, Yitian Yang, Yiqi Zhu, Fangchen Liu, Zhaoge Bi, Moe Thandar Kyaw Wynn, Kim-Kwang Raymond Choo, Huaming Chen Artificial intelligence is now embedded as a primary decision engine in continuously operated financial AI pipelines spanning training and updating, deployment and inference, and operation with monitoring and feedback. The automation and scale that make these pipelines effective also create novel attack surfaces, where small algorithmic perturbations can amplify into persistent, system-level financial harm. Existing surveys, however, either treat AI as a defensive tool or analyse adversarial machine learning in a domain-agnostic manner, abstracting away finance-specific constraints such as accounting plausibility, non-IID federated data, continuous retraining, and automation-amplified downstream effects. We address this gap with a unified, lifecycle-centric and mechanism-driven framework. We partition financial AI into three lifecycle stages: training and updating, deployment and inference, and operation, monitoring, and feedback. We further propose the Financial AI Security and Robustness Taxonomy, organising seventeen attack subtypes across data and model poisoning, adversarial attacks on decision boundaries, prompt injection in LLM-mediated workflows, and deepfake-driven subversion of KYC verification layers. For each subtype, we analyse algorithmic strategy, feasibility constraints, stealth and persistence, and downstream financial consequences. Finally, we identify open challenges and outline a research agenda toward lifecycle-aware stress testing and finance-relevant robustness benchmarks. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2605.30650 [cs.CR]   (or arXiv:2605.30650v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.30650 Focus to learn more Submission history From: Qingwen Zeng [view email] [v1] Thu, 28 May 2026 23:10:04 UTC (3,725 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)