The Fundamental Limits of Fraud Detection in Card Payment Networks
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 a minimax regret lower bound showing that these impairments enter multiplicatively in the denominator of the achievable learning rate. The bound...
| Authors | Gaurav Dhama |
|---|---|
| Primary category | cs.LG |
| Published | 26 May 2026 |
| Last updated at source | 29 May 2026 |
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