变分自编码器中基于潜在聚类的分段信用评分
Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder
摘要：
识别零售银行业务组合中具有不同风险状况的客户群，可以提高信用评分的准确性。变分自编码器（VAE）在不同的研究领域都显示出了良好的效果，并且已经证明了VAE的潜在空间中隐藏着强大的信息。我们使用的VAE，并表明，将输入数据转化为一个有意义的表示，它是可能的转向配置的VAE的潜在空间。具体而言，证据权重（WoE）转换封装的倾向陷入财务困境和VAE中的潜在空间保留在一个定义良好的聚类结构的这一特点。这些集群具有相当不同的风险状况，因此不仅适合信用评分，而且适合营销和客户目的。这种新的聚类方法为现有聚类算法中的一些挑战提供了解决方案，例如，建议集群数量，为新客户分配集群标签，实现集群可视化，扩展到大型数据集，捕获非线性关系等。最后，对于每个聚类中具有大量客户的投资组合，为每个聚类开发一个分类器模型可以改善信用评分评估。
英文摘要：
Identifying customer segments in retail banking portfolios with different risk profiles can improve the accuracy of credit scoring. The Variational Autoencoder (VAE) has shown promising results in different research domains, and it has been documented the powerful information embedded in the latent space of the VAE. We use the VAE and show that transforming the input data into a meaningful representation, it is possible to steer configurations in the latent space of the VAE. Specifically, the Weight of Evidence (WoE) transformation encapsulates the propensity to fall into financial distress and the latent space in the VAE preserves this characteristic in a well-defined clustering structure. These clusters have considerably different risk profiles and therefore are suitable not only for credit scoring but also for marketing and customer purposes. This new clustering methodology offers solutions to some of the challenges in the existing clustering algorithms, e.g., suggests the number of clusters, assigns cluster labels to new customers, enables cluster visualization, scales to large datasets, captures non-linear relationships among others. Finally, for portfolios with a large number of customers in each cluster, developing one classifier model per cluster can improve the credit scoring assessment.
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