[论文解读] Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring

Marc Schmitt|arXiv (Cornell University)|May 21, 2022

Financial Distress and Bankruptcy Prediction被引用 21

一句话总结

梯度提升机器（GBM）通常在多份数据集上优于深度学习（DL）在信用评分任务中的表现，训练时间更快；DL并非普遍劣势，但在结构化信用评分任务中，GBM通常是首选。

ABSTRACT

Artificial intelligence (AI) and machine learning (ML) have become vital to remain competitive for financial services companies around the globe. The two models currently competing for the pole position in credit risk management are deep learning (DL) and gradient boosting machines (GBM). This paper benchmarked those two algorithms in the context of credit scoring using three distinct datasets with different features to account for the reality that model choice/power is often dependent on the underlying characteristics of the dataset. The experiment has shown that GBM tends to be more powerful than DL and has also the advantage of speed due to lower computational requirements. This makes GBM the winner and choice for credit scoring. However, it was also shown that the outperformance of GBM is not always guaranteed and ultimately the concrete problem scenario or dataset will determine the final model choice. Overall, based on this study both algorithms can be considered state-of-the-art for binary classification tasks on structured datasets, while GBM should be the go-to solution for most problem scenarios due to easier use, significantly faster training time, and superior accuracy.

研究动机与目标

• 在行业环境中推动DL与GBM在信用风险评分方面的比较。
• 评估模型选择如何与信用评分的数据集特征互动。
• 提供证据以指导从业者在结构化数据上选择二分类的最新模型。

提出的方法

• 在信用评分任务上对两种最先进算法（DL和GBM）进行基准测试。
• 使用三份具有不同特征特性的数据集来测试模型鲁棒性。
• 从预测准确性和计算效率两个维度评估性能。

实验结果

研究问题

• RQ1 在不同结构化数据集上，GBM是否普遍优于DL在信用评分中的表现？
• RQ2 在何种数据集条件下，DL在信用风险建模中相对于GBM具有优势？
• RQ3 在该领域，DL与GBM在准确性与训练速度之间的权衡是什么？

主要发现

• GBM在信用评分方面往往比DL更强大。
• 由于资源需求较低，GBM在计算速度上具有优势。
• GBM的领先并不能在每种情形下得到保证；数据集特征决定了最佳模型。
• 两种算法都可以被视为结构化数据二分类的最新技术。
• 由于易于使用、显著更快的训练时间和更高的准确性，GBM应成为大多数问题场景的首选解决方案。