Class Weighting versus Amount Conditioning in Credit-Card Fraud Detection: A Dollar-Metric Study with a Temporal Explanation Audit

2026-07-16

AI Summary

This study addresses a critical limitation in existing credit fraud detection models, which typically optimize for transaction count rather than actual financial loss. Under controlled class imbalance, the work systematically disentangles and evaluates the distinct roles of monetary amount—first as sample weights during training and second as a reranking variable post-prediction. Building upon XGBoost, the approach integrates amount-derived features (e.g., ratios, velocities), multiple weighting strategies, and a score×amount reranking mechanism, with performance assessed using dollar-based metrics such as dollar recall and dollar precision. Empirical results demonstrate that incorporating amount-aware features and reranking substantially improves dollar recall, yet aggressive amount-based weighting degrades ranking quality. The findings indicate that transaction amount alone is unsuitable as a standalone sample weighting rule.

Abstract

Credit-card fraud losses are monetary, but papers often judge models with transaction-level scores. We ask whether transaction amount should shape training weights or be used later to order alerts. To separate this question from ordinary class imbalance handling, we keep total fraud-case weight fixed and vary only its allocation across fraud cases. The experiments test two chronological card-fraud datasets with XGBoost under unweighted training, standard class weighting, matched log-amount weighting, stronger amount-weighted variants, and score times amount reranking. Metrics are average precision, dollar recall, and dollar precision at fixed alert budgets over five seeds, with 95 percent day-block bootstrap intervals for the main contrasts. Results are narrower than expected. Amount-derived ratio and velocity features carry much of the signal, while raw amount fields add little once those features are in the model. In the matched setting, amount-conditioned training gives only small gains over class weighting and does not consistently beat the plain unweighted model. Stronger amount weights recover more fraudulent dollars, but at lower ranking quality and dollar precision. Reranking alerts by score times amount after training gives the largest dollar-recall shift. A small SHAP audit finds larger month-to-month attribution movement for fraud cases than for aggregate traffic. In these tests, amount is useful as a feature and as an alert-ordering variable, not by itself as a better sample-weighting rule.

Problem

• credit-card fraud detection
• class weighting
• amount conditioning
• dollar-metric evaluation
• alert reranking

Innovation

• dollar-metric evaluation
• amount conditioning
• alert reranking
• temporal explanation audit
• fraud detection