🤖 AI Summary
This study reveals that the long-standing performance limitations in bank card payment fraud detection stem fundamentally from inherent information deficiencies within the payment ecosystem, rather than insufficient model capacity. By formulating card authorization as a sequential decision-making problem characterized by delayed, censored, corrupted, and counterfactually missing feedback, the work establishes the first learnability lower bound for this setting. Through minimax regret analysis grounded in online learning and counterfactual reasoning—without requiring access to real transaction data—it demonstrates that information quality, particularly in issuer reporting and dispute resolution, constitutes the primary bottleneck, with heterogeneity further exacerbating learning difficulty. The theoretical analysis shows that the regret lower bound scales multiplicatively with the rate of information deficiency, indicating that improving reporting fidelity offers a more effective path to breaking through performance ceilings than increasing model complexity.
📝 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 a minimax regret lower bound showing that these impairments enter multiplicatively in the denominator of the achievable learning rate. The bound implies that improving issuer reporting quality or reducing censorship can yield larger reductions in the regret floor than increasing model complexity. We also show that heterogeneity across issuers worsens learnability beyond what average impairment rates suggest.
The paper contributes a theory of why fraud detection in payment networks is fundamentally harder than in standard online learning settings, identifies ecosystem information quality as the key bottleneck, and provides a theoretical basis for prioritizing investments in reporting infrastructure, dispute process quality, and selective exploration. The paper is theory-first and does not rely on proprietary transaction data.