From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems

[2605.23955] From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems Skip to main content Search Submit Donate Log in Computer Science > Artificial Intelligence arXiv:2605.23955 (cs) [Submitted on 11 May 2026 ( v1 ), last revised 26 Aug 2026 (this version, v4)] 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 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. While early financial ML addressed statistical challenges such as backtest overfitting, deep neural networks and Generative AI have introduced mechanical nondeterminism rooted in hardware and architecture. This survey provides a systems perspective on reproducibility failures across three modalities now dominant in financial AI: tabular models (post-hoc explanation variance), graph networks (stochastic sampling and temporal asynchrony), and LLM-based agentic workflows (batch-dependent divergence and trajectory drift). We supplement the literature analysis with first-party experiments on public financial datasets -- quantifying explanation rank instability in credit scoring, prediction flip rates in GNN-based fraud detection, and tensor-parallel-induced output divergence in LLM entity extraction. We propose a layered evaluation framework linking modality-specific metrics (RBO, D_cos, TDI, PSD) to audit readiness, and report where these measures overlap rather than complement one another. Subjects: Artificial Intelligence (cs.AI) ; Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Social and Information Networks (cs.SI); Computational Finance (q-fin.CP) Cite as: arXiv:2605.23955 [cs.AI] (or arXiv:2605.23955v4 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2605.23955 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ruizhe Zhou [ view email ] [v1] Mon, 11 May 2026 17:46:38 UTC (72 KB) [v2] Wed, 27 May 2026 17:26:09 UTC (63 KB) [v3] Thu, 20 Aug 2026 01:44:43 UTC (74 KB) [v4] Wed, 26 Aug 2026 20:23:02 UTC (75 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.AI < prev | next > new | recent | 2026-05 Change to browse by: cs cs.DC cs.LG cs.SI q-fin q-fin.CP References & Citations SciX 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 Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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