arXiv:2605.29272v1 Announce Type: new Abstract: Fraud detection models in payment networks train on chargeback labels that are systematically biased. Every label must survive three sequential gates: authorization (declined transactions generate no labels), issuer reporting (unreported fraud is invisible), and delay (pending chargebacks are missing at training time). Labels that do arrive may be corrupted by first-party misuse or issuer misclassification. A companion paper [arXiv:2605.27557] proved that these four impairments impose a minimax lower bound on detection performance. This paper ask
This research addresses a long-standing challenge in fraud detection, leveraging advanced AI techniques to improve the accuracy of payment networks.
Improved fraud detection directly impacts financial stability and reduces systemic losses in payment networks, offering significant economic benefits.
The ability to accurately recover causal labels in payment networks will lead to more robust and reliable fraud detection models.
- · Financial Institutions
- · Payment Processors
- · Data Scientists
- · Consumers
- · Fraudsters
Reduced financial losses due to fraud in payment transactions.
Increased trust and efficiency in online and digital payment systems.
Potential for new financial products and services built on more secure payment rails.
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