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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/2605.23955 |
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| _version_ | 1866910266166345728 |
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| author | Zhou, Ruizhe Liu, Xiaoyang Du, Gaoyuan Zheng, Yi Ren, Shouxi Chakrabarti, Deepayan Jiang, Dengdu |
| author_facet | Zhou, Ruizhe Liu, Xiaoyang Du, Gaoyuan Zheng, Yi Ren, Shouxi Chakrabarti, Deepayan Jiang, Dengdu |
| contents | 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 empirically validate the complementarity of logit-level and semantic-level determinism measures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23955 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems Zhou, Ruizhe Liu, Xiaoyang Du, Gaoyuan Zheng, Yi Ren, Shouxi Chakrabarti, Deepayan Jiang, Dengdu Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning Social and Information Networks Computational Finance 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 empirically validate the complementarity of logit-level and semantic-level determinism measures. |
| title | From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems |
| topic | Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning Social and Information Networks Computational Finance |
| url | https://arxiv.org/abs/2605.23955 |