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完整的信用评分数据集用于可靠的测试:样本外,时间外和宇宙外评估
Jonah Mushava1, Michael Murray1
1School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Westville Campus, Private Bag X54001, Durban, 4000, South Africa.
Data in brief
|July 4, 2024
概括
本研究为信用评分研究提供精选的抵押贷款数据集. 这些数据可以通过排除金融危机和流行病影响来实现稳健的模型开发.
科学领域:
- * 抵押贷款融资和信用风险分析
- * 数据科学和机器学习应用
背景情况:
- *弗雷迪麦克的单一家庭贷款级别数据集 (SFLLD) 追踪了美国抵押贷款的发起和偿还.
- *现有的数据集往往缺乏全面的信用风险建模的细节性或范围.
- * 专有数据访问限制阻碍了信用评分研究.
研究的目的:
- *为信用评分研究策划和发布标准化,高质量的抵押贷款数据集.
- * 促进贷款违约预测模型的开发和验证.
- *为研究人员提供可访问的数据,克服专有限制.
主要方法:
- *使用Freddie Mac SFLLD从1999年到2022年的季度快照.
- * 整合了超过5000万套固定利率抵押贷款的贷款发放和业绩数据.
- * 基于在特定窗口内错过的付款而定义的违约;按信用评分分层的数据.
- * 创建了不同的培训,样本外和超时测试数据集,不包括核心评估的危机/大流行时期.
- * 开发了一套额外的"宇宙之外"测试套件,使用2019年的贷款来评估流行病的表现.
主要成果:
- *为信用风险预测生成了1464个共变量和二进制目标标签的多个数据集.
- *通过分层数据分割和超时验证,确保严格的模型评估.
- *提供了一个独特的数据集,用于评估COVID-19大流行期间的贷款业绩.
结论:
- * 发布的数据集为推进信用评分研究提供了宝贵的资源.
- *标准化数据集使研究人员能够在常见数据上开发和测试模型.
- * 促进抵押贷款信用风险评估中的可重复性研究和创新.
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