用机器学习预测用户在开放银行中分享数据的意愿，并解释关键影响因素。
Predicting and Explaining Customer Data Sharing in the Open Banking
- 结合ADASYN与NEARMISS处理数据不平衡，提升XGBoost模型预测精度。
- 对数据流入和流出的预测准确率分别达91.39%和91.53%。
- 揭示移动渠道交易频次与信用卡使用是决定行为的核心因素，适合金融机构参考。
开放银行的兴起改变了金融数据管理格局，重塑了金融机构的市场动态与营销策略。为应对竞争压力，机构需平衡数据流入以优化服务，同时控制数据外流以防被对手利用。本研究提出一种框架，用于预测客户通过开放银行共享数据的可能性，并借助可解释性模型分析（EMA）剖析其行为成因。基于巴西某大型金融机构约320万客户的数据，采用融合ADASYN与NEARMISS的混合数据平衡策略，优化XGBoost模型训练。模型对数据流入和流出的预测准确率分别达到91.39%和91.53%。EMA阶段结合SHAP与分类回归树（CART），识别出影响客户决策的关键特征：移动端交易与消费次数、渠道内互动频率，以及信用相关特征，特别是全国范围内的信用卡使用情况。结果表明，移动端活跃度与信用行为是驱动数据共享的核心动因，为金融机构在开放银行环境中的战略制定与创新提供有力支持。
原文摘要 · Abstract (English)
The emergence of Open Banking represents a significant shift in financial data management, influencing financial institutions' market dynamics and marketing strategies. This increased competition creates opportunities and challenges, as institutions manage data inflow to improve products and services while mitigating data outflow that could aid competitors. This study introduces a framework to predict customers' propensity to share data via Open Banking and interprets this behavior through Explanatory Model Analysis (EMA). Using data from a large Brazilian financial institution with approximately 3.2 million customers, a hybrid data balancing strategy incorporating ADASYN and NEARMISS techniques was employed to address the infrequency of data sharing and enhance the training of XGBoost models. These models accurately predicted customer data sharing, achieving 91.39% accuracy for inflow and 91.53% for outflow. The EMA phase combined the Shapley Additive Explanations (SHAP) method with the Classification and Regression Tree (CART) technique, revealing the most influential features on customer decisions. Key features included the number of transactions and purchases in mobile channels, interactions within these channels, and credit-related features, particularly credit card usage across the national banking system. These results highlight the critical role of mobile engagement and credit in driving customer data-sharing behaviors, providing financial institutions with strategic insights to enhance competitiveness and innovation in the Open Banking environment.
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