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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.30650 |
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| _version_ | 1866916063886704640 |
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| author | Zeng, Qingwen Zhao, Zhenghao Yang, Yitian Zhu, Yiqi Liu, Fangchen Bi, Zhaoge Wynn, Moe Thandar Kyaw Choo, Kim-Kwang Raymond Chen, Huaming |
| author_facet | Zeng, Qingwen Zhao, Zhenghao Yang, Yitian Zhu, Yiqi Liu, Fangchen Bi, Zhaoge Wynn, Moe Thandar Kyaw Choo, Kim-Kwang Raymond Chen, Huaming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30650 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech Zeng, Qingwen Zhao, Zhenghao Yang, Yitian Zhu, Yiqi Liu, Fangchen Bi, Zhaoge Wynn, Moe Thandar Kyaw Choo, Kim-Kwang Raymond Chen, Huaming Cryptography and Security 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. |
| title | When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2605.30650 |