发表机构
Tongji University（同济大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对欺诈操作中自动化授权的证据时效性与审查能力问题，提出FCAC框架，在IEEE-CIS等数据集上实现零漂移自动化率84.4%等结果，明确审计新鲜度与分析师能力为联合设计考量因素。
AI 中文摘要
欺诈操作必须在自动批准、分析师审查与自动拦截之间分配事件，即便评估这些操作所需的标签具有选择性且存在延迟。预测分数可对案例排序，但无法显示证据是否足够新颖且具有代表性，以将操作委托给模型。我们提出新鲜度受限审计能力（FCAC），这是一种将自动化视为受操作风险、证据新鲜度与共享审查能力约束的授权决策的决策支持框架。该框架从成熟的随机审计与预先指定的时间容差中评估候选操作区域；受支持区域实现自动化，未受支持区域仍保留在审查中。生成的决策记录报告证据年龄、审计需求、总审查工作量、价值暴露及兼容的时间变化。我们表明，若不限制未观测到的标签演变，当前操作风险无法识别。在代表性随机审计、与标签无关的证据窗口，以及连接历史与当前操作风险的预先指定条件下，我们推导了不安全授权的同时有限样本控制。针对IEEE-CIS、ULB-Worldline与Elliptic++的模拟审计的时间评估显示，零漂移自动化率分别为84.4%、67.4%与81.3%，总审查工作量分别为24.1%、46.0%与43.1%。实验揭示了审计能力权衡：稀疏审计延迟授权，而密集审计最终会增加工作量。单独指定的BAF压力测试进一步表明， fallback阈值必须反映候选特定证据，而非风险限制的通用比例。这些发现将审计新鲜度与分析师能力确定为欺诈决策支持的联合设计考量因素。
英文摘要
Fraud operations must allocate events among automatic approval, analyst review, and automatic blocking even though the labels needed to evaluate these actions are selective and delayed. Predictive scores order cases, but they do not show whether the evidence is current and representative enough to delegate an action to the model. We develop freshness-constrained audit capacity (FCAC), a decision-support framework that treats automation as an authorization decision constrained by action risk, evidence freshness, and shared review capacity. It evaluates candidate action regions from mature randomized audits and a prespecified temporal allowance. Supported regions are automated; unsupported regions remain in review. The resulting decision record reports evidence age, audit demand, total review workload, value exposure, and compatible temporal change. We show that current action risk is unidentified without restricting unobserved label evolution. Under representative randomized audits, label-independent evidence windows, and a prespecified condition linking historical and current action risk, we derive simultaneous finite-sample control of unsafe authorization. Chronological evaluations with simulated audits on IEEE-CIS, ULB-Worldline, and Elliptic++ yield zero-drift automation rates of 84.4%, 67.4%, and 81.3%, with total review workloads of 24.1%, 46.0%, and 43.1%. The experiments reveal an audit-capacity trade-off: sparse auditing delays authorization, whereas intensive auditing eventually increases workload. A separately specified BAF stress test further indicates that fallback thresholds must reflect candidate-specific evidence rather than a common fraction of the risk limit. These findings identify audit freshness and analyst capacity as joint design considerations for fraud decision support.
Comments23 pages, 4 figures, 6 tables. Preprint
