Credit scoring is a very critical task for banks and other financial institutions, and it has become an important evaluation metric to distinguish potential defaulting users. In this paper, we propose a credit score prediction method based on feature transformation and ensemble model, which is essentially a cascade approach. The feature transformation process consisting of boosting trees (BT) and auto-encoders (AE) is employed to replace manual feature engineering and to solve the data imbalance problem. For the classification process, this paper designs a heterogeneous ensemble model by weighting the factorization machine (FM) and deep neural networks (DNN), which can efficiently extract low-order intersections and high-order intersections. Comprehensive experiments were conducted on two standard datasets and the results demonstrate that the proposed approach outperforms existing credit scoring models in accuracy.

中文翻译：

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一种基于特征变换和集成模型的信用评分新方法

信用评分对于银行和其他金融机构来说是一项非常关键的任务，它已成为区分潜在违约用户的重要评估指标。在本文中，我们提出了一种基于特征变换和集成模型的信用评分预测方法，本质上是一种级联方法。采用由提升树（BT）和自动编码器（AE）组成的特征转换过程来代替人工特征工程并解决数据不平衡问题。对于分类过程，本文通过对分解机（FM）和深度神经网络（DNN）进行加权设计了异构集成模型，可以高效地提取低阶交叉点和高阶交叉点。