🤖 AI Summary
This study addresses systematic biases in fraud detection models within payment networks arising from chargeback labels, including authorization interception, issuer underreporting, delayed settlement, and label contamination. The authors formulate label generation as a sequential missing-data problem with a three-stage propensity scoring mechanism and an explicit contamination layer. They propose the Sequential Triple-Robust (STR) estimator—the first method capable of simultaneously correcting all four bias sources. By decoupling model training from the chargeback maturation cycle, STR enables effective use of data just days old. The estimator achieves strictly lower mean squared error than naive approaches at any sample size, and provides theoretically grounded guidance for optimal training window selection, finite-sample confidence intervals, and formal statistical guarantees.
📝 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 asks: can that bound be achieved?
We formalize the observation pipeline as a sequential missing-data problem with three propensity stages and a corruption layer, and construct the Sequential Triply Robust (STR) estimator. The STR corrects for all four impairments simultaneously and achieves the semiparametric efficiency bound -- no estimator can have lower asymptotic variance. It is sequentially triply robust: at each gate, consistency requires only that either the propensity model or the outcome regression is correctly specified, not both. We provide corruption correction via noise-rate-adjusted pseudo-labels, empirical Bayes shrinkage to stabilize inverse-propensity weights for small issuers, a plug-in variance estimator yielding valid confidence intervals, and a Bernstein concentration inequality for finite-sample guarantees.
On the operational side, we derive the optimal training delay -- the maturity window that minimizes the sum of label-quality loss and model staleness -- and prove that the STR permits training on data that is days old rather than months old, decoupling model freshness from the chargeback maturity cycle. The STR provably dominates naive chargeback-based training in mean squared error for any sample size.