针对卢旺达移动支付反洗钱难题，构建了适配监管约束的机器学习监测框架。
Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework
- 基于账户行为特征设计多维度指标，融合监督与无监督模型进行联合检测
- 在极端样本不平衡下实现0.89以上精确率，每万笔交易仅触发0.46次警报
- 方案贴合非洲金融监管实际，可直接用于真实业务场景的合规审查
移动支付在撒哈拉以南非洲显著拓展了金融包容性，也扩大了洗钱与恐怖融资（ML/TF）活动的空间，尤其在高频率、低价值交易为主的生态系统中。卢旺达即为典型案例：数百万活跃用户，由电信运营商主导的MTN与Airtel网络钱包，以及负责监控交易流的金融情报中心（FIC），其数据规模已超出传统规则系统的处理能力。本文针对卢旺达反洗钱/反恐融资（AML/CFT）制度，开发并评估了一套交易监控框架，应对三大挑战：极端类别不平衡（预估仅0.1%）、标签稀缺且延迟、调查人力有限。利用含17种洗钱模式的合成数据集SAML-D（9,504,852条交易），构建账户为中心的行为特征（滚动速度、净流动方向、对手方多样性、突发性），对比多种监督分类器（逻辑回归、随机森林、LightGBM）、无监督异常检测器（孤立森林、局部离群因子）、密集自编码器及后期融合元学习器。评估采用实操指标：PR-AUC、校准后约90%精确率下的召回率、前K%召回率、每万笔交易警报数。在时间上隔离测试期，LightGBM取得PR-AUC=0.0469，捕获64例洗钱事件，精确率约0.89，每万笔0.51次警报；融合堆叠模型达到PR-AUC=0.0477，精确率约0.91，警报率0.46次/万笔，识别出59个真实正例。研究将得分区间映射至卢旺达本地分析师工作流程及可疑交易报告（STR/SAR）升级机制，并提出从合成原型到与国家银行和金融情报中心真实数据验证的分阶段路径。贡献在于提供一套符合非洲监管约束的治理感知流程与评估协议，而非新算法。
原文摘要 · Abstract (English)
Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activity in ecosystems dominated by high-volume, low-value transactions. Rwanda is a case in point: several million active mobile-money users, telecom-led wallets on the MTN and Airtel networks, and a Financial Intelligence Centre (FIC) supervising transaction streams whose scale exceeds static rule-based monitoring. This paper develops and evaluates a transaction-monitoring framework aligned to the Rwandan AML/CFT regime under (i) extreme class imbalance (~0.1% prevalence), (ii) scarce and delayed labels, and (iii) bounded investigator capacity. Using SAML-D, a synthetic dataset of 9,504,852 transactions with 17 laundering typologies, we engineer account-centric behavioural features (rolling velocity, net-flow directionality, counterparty diversity, burstiness) and benchmark supervised classifiers (Logistic Regression, Random Forest, LightGBM), unsupervised anomaly detectors (Isolation Forest, Local Outlier Factor), a dense autoencoder, and a late-fusion meta-learner. Evaluation is operational: PR-AUC, recall at a calibrated ~90%-precision point, recall at top-K%, and alerts per 10,000. On the chronologically held-out test period, LightGBM attains PR-AUC = 0.0469, capturing 64 laundering cases at precision ~0.89 with 0.51 alerts per 10,000; the fusion stacker reaches PR-AUC = 0.0477 at precision ~0.91 and 0.46 alerts per 10,000, recovering 59 true positives. We map score bands to Rwanda-relevant analyst workflows and STR/SAR escalation, and outline a staged path from synthetic prototyping to real-data validation with the National Bank of Rwanda and FIC. The contribution is operational: a governance-aware pipeline and evaluation protocol calibrated to the constraints of an African mobile-money regulator, not a new algorithm.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。