发表机构
CoreDevX LABTAV - Advanced Technologies Laboratory; Banco de Créditos e Inversiones (BCI)（CoreDevX 高级技术实验室; 信贷与投资银行）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对数字开户后身份冒充欺诈的少样本问题，利用量子单类分类器（AQFM）在真实数据上实现88%召回率，并比经典基线减少约70%假阳性，且在2026年独立队列中保持最佳性能。
AI 中文摘要
身份冒充欺诈可能仅在数字账户开户后才显现，需要一种基于激活后不久观察到的行为和交易信号的早期开户后监测方法。已确认的案例仍然稀少，造成极端类别不平衡，并为数字银行带来重大运营风险。我们在此背景下评估量子机器学习（QML）与经典单类方法，使用来自智利Banco de Creditos e Inversiones（BCI）的2025年真实队列，包含3,419个数字开户，其中8个已确认的冒充欺诈案例（0.23%发生率）。数据通过BCI与CoreDevX LABTAV之间的行业研究合作在运营约束下提供。最佳性能配置仅使用2个量子比特，并在CoreDevX的SpinQ Triangulum II（一种3量子比特核磁共振（NMR）设备）上执行，实现了超越经典模拟的硬件验证。基于自动量子特征映射（AQFM）的量子单类分类器实现了88%的召回率（检测出8个欺诈中的7个）和9.21%的精确率，在可比召回率下，假阳性比最佳经典基线减少约70%。当其2025年校准的决策阈值原封不动地应用于独立的2026年队列时，AQFM检测出31个欺诈中的20个（64.5%召回率），精确率为8.81%，并且在评估的模型中仍保持最佳性能。
英文摘要
Identity impersonation fraud may emerge only after digital account opening, requiring an early post-onboarding approach based on behavioral and transactional signals observed shortly after activation. Confirmed cases remain rare, creating extreme class imbalance and material operational risk for digital banks. We evaluate quantum machine learning (QML) against classical one-class methods in this setting using a real 2025 cohort of 3,419 digital account openings from Banco de Creditos e Inversiones (BCI), Chile, including 8 confirmed impersonation-fraud cases (0.23% prevalence). The data were provided through an industry-research collaboration between BCI and CoreDevX LABTAV under operational constraints. The best-performing configuration used only 2 qubits and was executed on CoreDevX's SpinQ Triangulum II, a 3-qubit nuclear magnetic resonance (NMR) device, enabling hardware validation beyond classical simulation. The quantum one-class classifier based on Automatic Quantum Feature Mapping (AQFM) achieves 88% recall (7 of 8 frauds detected) and 9.21% precision, corresponding to approximately 70% fewer false positives than the best-performing classical baseline at comparable recall. When its 2025-calibrated decision threshold is applied unchanged to the independent 2026 cohort, AQFM detects 20 of 31 frauds (64.5% recall) with 8.81% precision and remains the best-performing model among those evaluated.
Comments14 pages, 3 Appendixes
