用16步流程构建高风险客户识别模型，鲁棒性强且可解释。
Anti-Money Laundering Machine Learning Pipelines; A Technical Analysis on Identifying High-risk Bank Clients with Supervised Learning
- 基于SQL的特征工程与预训练模型联动，构建端到端ML流水线。
- 在19万客户数据上实现0.961的平均AUROC，标准差仅0.005。
- 提供可解释模块，适合金融风控与合规团队参考应用。
反洗钱（AML）是金融机构的核心任务之一，机器学习（ML）在此领域展现出巨大潜力。本文针对多伦多大学2023-2024年管理与创新研究所（IMI）大数据与人工智能竞赛任务1的数据集，提出一套系统化、全流程的ML流水线，用于识别高风险银行客户。该数据集包含195,789个客户编号，我们采用16步设计与统计分析流程，确保模型稳健性。通过将数据存入SQLite数据库，开发基于SQL的特征工程算法，连接预训练模型并实现推理就绪，同时集成可解释人工智能（XAI）模块以提取特征重要性。最终流水线在测试集上取得平均受试者工作特征曲线下面积（AUROC）为0.961，标准差（SD）为0.005，位列竞赛第二名。
原文摘要 · Abstract (English)
Anti-money laundering (AML) actions and measurements are among the priorities of financial institutions, for which machine learning (ML) has shown to have a high potential. In this paper, we propose a comprehensive and systematic approach for developing ML pipelines to identify high-risk bank clients in a dataset curated for Task 1 of the University of Toronto 2023-2024 Institute for Management and Innovation (IMI) Big Data and Artificial Intelligence Competition. The dataset included 195,789 customer IDs, and we employed a 16-step design and statistical analysis to ensure the final pipeline was robust. We also framed the data in a SQLite database, developed SQL-based feature engineering algorithms, connected our pre-trained model to the database, and made it inference-ready, and provided explainable artificial intelligence (XAI) modules to derive feature importance. Our pipeline achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.961 with a standard deviation (SD) of 0.005. The proposed pipeline achieved second place in the competition.
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