By using machine learning algorithms, banks and other lending institutions can construct intelligent risk control models for loan businesses, which helps to overcome the disadvantages of traditional evaluation methods, such as low efficiency and excessive reliance on the subjective judgment of auditors. However, in the practical evaluation process, it is inevitable to encounter data with missing credit characteristics. Therefore, filling in the missing characteristics is crucial for the training process of those machine learning algorithms, especially when applied to rural banks with little credit data. In this work, we proposed an autoencoder-based algorithm that can use the correlation between data to restore the missing data items in the features. Also, we selected several open-source datasets (German Credit Data, Give Me Some Credit on the Kaggle platform, etc.) as the training and test dataset to verify the algorithm. The comparison results show that our model outperforms the others, although the performance of the autoencoder-based feature restorer decreases significantly when the feature missing ratio exceeds 70.
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