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.

翻译：多项研究表明，信用风险预测仍是金融服务行业关注的主要问题，并持续吸引大量学术研究兴趣。该研究领域至关重要，因为它能帮助金融机构评估借款人的违约概率，从而直接影响贷款决策与风险管理策略。尽管该领域已取得进展，但在信用卡申请提交前的消费者行为研究方面仍存在显著的知识缺口。本研究旨在预测客户对邮件营销活动的响应情况，并评估参与客户的违约可能性。研究采用先进的机器学习技术，特别是逻辑回归与XGBoost，分析消费者行为并预测其对直邮活动的响应。通过整合包括插补与分箱在内的多种数据预处理策略，我们增强了预测模型的鲁棒性与准确性。结果表明，XGBoost在各项评估指标上均持续优于逻辑回归，尤其是在采用分类分箱与定制插补的场景中。这些发现表明XGBoost在处理复杂数据结构方面具有特殊优势，并为信用风险评估提供了强大的预测能力。