arXiv:2605.27557v1 Announce Type: new Abstract: Card payment fraud detection is usually framed as a supervised classification problem. Although this approach has generated practical progress, improvement has remained incremental despite major advances in model architecture. We argue that this is not mainly a failure of function approximation or optimization, but a consequence of structural information impairments inherent to the payment ecosystem. We formalize card authorization as a sequential decision problem with delayed, censored, corrupted, and counterfactually missing feedback. We derive
This paper's publication on arXiv comes as AI models are rapidly advancing, yet systemic issues in established domains like fraud detection persist, indicating fundamental limitations beyond isolated model improvements.
A strategic reader should care because this research challenges the prevailing assumption that better models alone can solve complex problems, highlighting the need for systemic solutions to 'information impairments' in digital ecosystems.
The focus potentially shifts from optimizing individual fraud detection models to addressing the underlying structural and informational flaws within card payment networks themselves.
- · Payment network architects
- · Data scientists specializing in systemic analysis
- · Financial institutions innovating beyond model-centric approaches
- · Fraudsters
- · AI model developers ignoring systemic context
- · Financial institutions reliant solely on incremental model updates
Financial institutions may allocate more resources to redesigning data collection and feedback mechanisms within their payment systems.
This could lead to a new generation of fraud detection systems that integrate structural improvements with advanced AI, rather than relying on AI alone.
Improved fundamental security and lower fraud rates could increase consumer trust and expand the global reach of digital payment networks.
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Read at arXiv cs.LG