超越客户流失：基于时序机器学习的零售银行金融碎片化预测
Beyond Churn: Predicting Financial Fragmentation in Retail Banking with Temporal Machine Learning
- Royal Bank of Canada（加拿大皇家银行）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出四阶段XGBoost级联时序机器学习系统，基于零售银行多源数据预测客户90天内的金融碎片化（资金向外部转移），取得多项高精度指标，为客户留存决策提供实用支撑。
AI中文摘要：
零售银行客户流失通常被视为一个终端二元事件，尽管客户关系往往更早因存款、投资及日常活动向外部金融机构的部分转移而弱化。本文将这种前置状态定义为金融碎片化，并提出一种端到端的时序机器学习系统，用于在客户完全脱离前预测该状态。研究采用某大型零售银行的匿名多源数据，该框架可预测90天内是否会发生有效的外部转账或投资事件。研究使用595220个客户-月度观测值，包含346个工程特征，这些特征结合了月度客户画像、余额、产品关系、过往资金流动行为、宏观经济状况及竞争对手活动。一个四阶段XGBoost级联模型用于估计：(1)90天内是否会发生外部流出；(2)预期金额；(3)流出的起始产品；(4)目标金融机构。主分类器的测试精确率-召回率曲线下面积为0.823，在校准阈值下，精确率达86.4%，召回率为75.1%，F1分数为0.803。按第一阶段碎片化得分降序排列测试观测值，前1%客户的精确率达95.3%，前5%客户覆盖了78.7%的观测流出案例；金额模型的预测中94.9%落在相邻金额区间内；目标机构预测在27个类别上的宏F1为0.81，源产品预测的加权F1为0.92。通过将分析焦点从终端客户流失转向更早的资金转移，所提方法为主动、可解释且经济合理的客户留存决策支持提供了实用基础。
英文摘要：
Retail banking attrition is usually represented as a terminal binary event, even though client relationships often weaken earlier through partial movements of deposits, investments, and recurring activity to external financial institutions. This paper defines that preceding state as financial fragmentation and presents an end-to-end temporal machine-learning system for predicting it before complete disengagement. Using anonymized multi-source data from a large retail bank, the framework predicts whether a valid external transfer or investment event will occur within 90 days. The study uses 595,220 client-month observations, with 346 engineered features combining monthly client profiles, balances, product relationships, prior flow-of-funds behavior, macroeconomic conditions, and competitor activity. A four-stage XGBoost cascade estimates (1) whether an external outflow will occur within 90 days, (2) the expected amount, (3) the originating product, and (4) the destination financial institution. The primary classifier achieved a test precision-recall area under the curve of 0.823. At the validation-selected threshold, it produced 86.4% precision, 75.1% recall, and an F1 score of 0.803. Ranking test observations in descending Stage 1 fragmentation score, the top 1% of clients yielded 95.3% precision, while the top 5% captured 78.7% of observed outflow cases. The amount model placed 94.9% of predictions within an adjacent amount bucket. Destination prediction reached a macro-F1 of 0.81 across 27 classes; source-product prediction achieved a weighted F1 of 0.92. By moving the analytical focus from terminal churn to earlier fund migration, the proposed approach provides a practical foundation for proactive, explainable, and economically informed client-retention decision support.
