用机器学习预测用户是否响应金融营销，提升风控精准度。
Optimizing Fintech Marketing: A Comparative Study of Logistic Regression and XGBoost
- 对比逻辑回归与XGBoost，用分箱和自定义填补提升模型表现。
- XGBoost在各类指标上均优于逻辑回归，尤其在分箱数据中优势明显。
- 适合金融风控、营销策略优化领域的从业者参考。
已有研究指出，信用风险预测仍是金融服务行业的核心挑战，对贷款决策与风险管理至关重要。然而，关于申请信用卡前消费者行为的研究仍存在显著空白。本研究旨在预测客户对邮寄营销活动的响应，并评估参与者的违约概率。通过采用逻辑回归与XGBoost等先进机器学习方法，结合多种数据预处理策略（如缺失值填补与分箱），提升模型鲁棒性与准确性。结果表明，XGBoost在多个评估指标上持续优于逻辑回归，尤其在使用分类分箱与自定义填补时表现更佳。这说明其在处理复杂数据结构方面具有强预测能力，适用于信用风险评估场景。
原文摘要 · Abstract (English)
As several studies have shown, predicting credit risk is still a major concern for the financial services industry and is receiving a lot of scholarly interest. This area of study is crucial because it aids financial organizations in determining the probability that borrowers would default, which has a direct bearing on lending choices and risk management tactics. Despite the progress made in this domain, there is still a substantial knowledge gap concerning consumer actions that take place prior to the filing of credit card applications. The objective of this study is to predict customer responses to mail campaigns and assess the likelihood of default among those who engage. This research employs advanced machine learning techniques, specifically logistic regression and XGBoost, to analyze consumer behavior and predict responses to direct mail campaigns. By integrating different data preprocessing strategies, including imputation and binning, we enhance the robustness and accuracy of our predictive models. The results indicate that XGBoost consistently outperforms logistic regression across various metrics, particularly in scenarios using categorical binning and custom imputation. These findings suggest that XGBoost is particularly effective in handling complex data structures and provides a strong predictive capability in assessing credit risk.
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