Fraud Type Decomposition and the Observation-Mechanism Taxonomy:Class-Specific Detection Limits in Payment Networks
arXiv:2605.31257v1 Announce Type: new Abstract: Fraud detection in payment networks relies on labels generated through heterogeneous and imperfect observation processes, yet existing approaches treat fraud as a homogeneous binary variable. We show that this assumption is structurally incorrect and leads to provable inefficiency. We introduce an observation-mechanism taxonomy that partitions fraud into five classes, each defined by a distinct censorship and labeling pipeline. We prove that estimating fraud rates separately by class and aggregating strictly dominates pooled estimation, with the
The paper leverages recent advancements in analytical methods, likely driven by the increasing complexity and volume of financial transactions and fraud attempts.
This research provides a more sophisticated framework for fraud detection, promising greater efficiency and accuracy which can significantly impact financial security and operational costs.
Fraud detection will move from a generalized binary approach to a class-specific methodology, allowing for more precise identification and mitigation strategies.
- · Financial Institutions
- · Payment Networks
- · Data Scientists
- · Consumers
- · Fraudsters
- · Generic Fraud Detection Software
Enhanced fraud detection systems will reduce financial losses for institutions and consumers.
Improved detection capabilities could lead to more dynamic and adaptive fraud schemes as bad actors respond.
The methodology could be extended to other forms of anomaly detection beyond financial fraud, impacting various industry sectors.
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Read at arXiv cs.LG