用机器学习自动调整信用卡额度，提升效率与一致性。
Automating Credit Card Limit Adjustments Using Machine Learning
- 采用成本敏感学习，结合神经网络与XGBoost模型进行比较优化。
- 模型与人工委员会决策达成近乎完美一致（Cohen's kappa接近1）。
- 适合金融风控、自动化决策系统等场景的从业者参考。
委内瑞拉银行长期以来通过人工委员会决定信用卡额度调整。随着经济改善，持卡人数预计增加，人工决策已难以为继。本项目提出一种基于成本敏感学习的机器学习模型，用于自动化信用额度调整。利用委内瑞拉信贷公司数据，训练并比较多种神经网络与XGBoost模型，通过网格搜索与10折交叉验证选择最优模型。最终模型在准确率、成本效益与可解释性之间取得良好平衡。模型性能通过Cohen's kappa系数评估，与人工委员会决策几乎完全一致，显示出高度可靠性。
原文摘要 · Abstract (English)
Venezuelan banks have historically made credit card limit adjustment decisions manually through committees. However, since the number of credit card holders in Venezuela is expected to increase in the upcoming months due to economic improvements, manual decisions are starting to become unfeasible. In this project, a machine learning model that uses cost-sensitive learning is proposed to automate the task of handing out credit card limit increases. To accomplish this, several neural network and XGBoost models are trained and compared, leveraging Venezolano de Credito's data and using grid search with 10-fold cross-validation. The proposed model is ultimately chosen due to its superior balance of accuracy, cost-effectiveness, and interpretability. The model's performance is evaluated against the committee's decisions using Cohen's kappa coefficient, showing an almost perfect agreement.
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