From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems
The paper titled 'From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems' explores the challenges of deploying machine learning in regulated financial environments. It highlights issues of algorithmic reproducibility and the impact of deep learning technologies on determinism. The authors propose a framework for evaluating audit readiness in financial AI systems based on various metrics.
- ▪The survey addresses vulnerabilities in algorithmic reproducibility in financial machine learning applications.
- ▪It examines three dominant modalities in financial AI: tabular models, graph networks, and LLM-based workflows.
- ▪The authors conducted experiments on public financial datasets to quantify explanation instability and prediction divergence.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23955 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Z1AEgBEp5YNQ |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.23955 (cs) [Submitted on 11 May 2026] Title:From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems Authors:Ruizhe Zhou, Xiaoyang Liu, Gaoyuan Du, Yi Zheng, Shouxi Ren, Deepayan Chakrabarti, Dengdu Jiang View a PDF of the paper titled From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems, by Ruizhe Zhou and 6 other authors View PDF HTML (experimental) Abstract:Deploying machine learning in regulated financial environments -- credit risk, fraud detection, and anti-money laundering -- exposes critical vulnerabilities in algorithmic reproducibility.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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