Problem
Research questions and friction points this paper is trying to address.
unsupervised anomaly detection
flow matching
tabular data
training-set contamination
financial anomaly detection
Unsupervised Anomaly Detection Using Flow Matching on Tabular Data
🤖 AI Summary
研究使用流匹配方法在受污染的训练数据中进行无监督表格异常检测，通过比较不同异常评分函数来提高检测稳定性。
📝 Abstract
Financial anomaly detection often relies on large unlabeled transaction logs, where anomalous samples may already be present during training. Such training-set contamination violates the clean-normal data assumption underlying many anomaly detection methods. Although flow matching has demonstrated strong performance in generative modeling, its robustness in unsupervised tabular anomaly detection remains underexplored. In this work, we study flow-matching-based anomaly detection under contaminated training data by comparing Time-Conditioned Contraction Matching (TCCM) with Forest-Flow and evaluating multiple anomaly scoring functions. Our results show that the choice of anomaly score is critical. The original single-step Decision score used by TCCM is sensitive to contamination, whereas trajectory-based Deviation and Reconstruction scores provide more stable anomaly signals. With these scores, Forest-Flow becomes competitive with, and in some cases outperforms, TCCM. These findings highlight the importance of anomaly scoring for flow-matching methods in financial anomaly detection under severe class imbalance.
Innovation
Methods, ideas, or system contributions that make the work stand out.
flow matching
unsupervised anomaly detection
anomaly scoring function
training data contamination
tabular data
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