Large-scale online service platforms face severe challenges from organized platform abuse: multiple forms such as credit card fraud and promotion abuse continually emerge, characterized by large numbers of involved accounts, rapid outbreaks, and constantly shifting tactics. Existing mainstream approaches, whether heuristic rules limited in precision, supervised learning with insufficient generalization, or graph models that are engineering-heavy and dependent on seed users, have failed to address such threats effectively. This paper returns to first principles and, starting from the economic constraints of fraudulent behavior, proposes the Fraudster's Trilemma: organized attackers cannot simultaneously achieve scale, low cost, and dispersed cash-out. Building on this theory, we derive a robust structural invariant in organized fraud, namely centralized cash-out, and use a simple statistical method to turn low-precision individual weak signals into high-precision strong decisions. The method requires no labels, is nearly parameter-free, white-box interpretable, has linear complexity O(|E|), avoids cold-start issues, and its detection logic possesses the "open-hand" property: attackers cannot evade it even when fully informed. We validate the approach on two real fraud incidents in backtests. In the promotion abuse case, a single near-zero-cost weak signal (global Precision of only 16%) after structural amplification achieves Precision above 91% and Recall exceeding 99% (z=10.0); at a higher threshold (z=40.0), Precision reaches 93.7%. In the credit card fraud case, an infrastructure-layer weak signal (device spoofing) successfully detects payment-layer attacks without any business-logic linkage, revealing the framework's natural MO-agnostic property: it relies more on the structural invariant than on signal semantics.

翻译：大规模在线服务平台面临组织化平台滥用的严峻挑战：信用卡欺诈、促销滥用等多种形式不断涌现，其特点为涉案账户数量大、爆发速度快且战术持续演变。现有主流方法——无论是精度有限的启发式规则、泛化能力不足的监督学习，还是工程负担重且依赖种子用户的图模型——均未能有效应对此类威胁。本文回归第一性原理，从欺诈行为的经济约束出发，提出“欺诈者三难困境”：组织化攻击者无法同时实现规模化、低成本和分散化变现。基于此理论，我们推导出组织化欺诈中的稳健结构不变性特征，即集中变现模式，并利用简单统计方法将低精度的个体弱信号转化为高精度的强决策。该方法无需标签、近乎无参数、白盒可解释、具备线性复杂度O(|E|)，避免了冷启动问题，其检测逻辑具有“摊手式”特性：即便攻击者完全知情也无法规避。我们通过两起真实欺诈事件的回溯验证了该方法。在促销滥用案例中，单个近乎零成本的弱信号（全局精确率仅16%）经结构放大后，精确率达到91%以上，召回率超过99%（z=10.0）；在更高阈值（z=40.0）下，精确率达93.7%。在信用卡欺诈案例中，基础设施层弱信号（设备伪造）无需任何业务逻辑关联即可成功检测支付层攻击，揭示了该框架固有的“攻击手法无关性”：其依赖的是结构不变性而非信号语义。