提高信用评分透明度的新框架:利用Shapley值来解释可解释的信用评分卡
Rivalani Hlongwane1, Kutlwano Ramabao1, Wilson Mongwe2
1Graduate School of Business, University of Cape, Cape Town, South Africa.
PloS one
|August 12, 2024
概括
本研究引入了一个新的框架,使用Shapley值来创建可解释的信用评分卡. 先进的机器学习模型现在为信用风险评估提供了高准确性和透明度.
科学领域:
- 机器学习 机器学习
- 金融分析 金融分析
- 信用风险管理 信用风险管理
背景情况:
- 信用评分卡对于评估贷款申请人的信用能力至关重要.
- 像XGBoost这样的先进模型提供了比逻辑回归更高的准确性,但缺乏可解释性.
- 复杂模型缺乏透明度,阻碍了金融机构的采用.
研究的目的:
- 开发一个新的框架,用于构建可解释的信用评分卡.
- 为了弥合先进的机器学习研究和实际的信用评分应用之间的差距.
- 在不牺牲透明度的情况下使用强大的预测模型.
主要方法:
- 应用了一个使用Shapley值的框架来创建可解释的信用评分卡.
- 在两个信用数据集上使用了XGBoost,随机森林,LightGBM和CatBoost模型.
- 离散的数值变量和用于模型开发的一次热编码.
- 使用沙普利值,为预测变量组推导信用评分.
主要成果:
- 开发的框架产生了信用评分卡,其解释性与后勤回归相似.
- 与传统方法相比,可解释的成绩单保持了更高的预测准确性.
- 沙普利的价值观有效地将复杂的模型输出转化为可理解的信用评分.
结论:
- 新的框架为建立透明和准确的信用评分卡提供了实际解决方案.
- 它允许金融机构利用先进的机器学习模型,同时确保监管合规.
- 这种方法在实践中提高了信用评分工具的信任和可用性.
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