Abstract: With the development of credit business in China, assessing the default risk of each loan has become a crucial task. Due to the complex internal relationships among different features in financial credit data, the effectiveness of traditional machine learning methods and ensemble learning methods relies on feature selection, while ignoring the internal relationships of data, and feature selection may also cause data loss. To solve the above problems, a feature extractor based on multi-scale deep feature fusion is proposed.  Firstly, multi-scale convolution is applied to one-dimensional data to fully extract the internal relationships between features and perform attention fusion to obtain more critical features. Then, an ensemble learning XGBoost classifier is used to classify deep abstracted features and obtain the prediction results. Experimental results show that the multi-scale deep feature fusion approach can better predict personal credit risk  under the real data set. The values  of AUC and KS are both increased, in comparison to the XGBoost model and traditional machine learning methods.

Key words: credit risk prediction, deep feature extraction, attention fusion, XGBoost