关键词: 信用评估, 自注意力机制, 词嵌入, 特征提取, 深度神经网络

Abstract: In the credit scoring problem, the user information contains both category data and numerical data. Traditional artificial intelligence-based credit scoring algorithms usually transform the category data into one-hot vectors and joints them with numerical data, as the input of the discriminator. In contrast, this paper extracts vectors of category data based on the word embedding techniques which are popularly used in the natural language processing problem. After that, the set of the word vectors is analogized to a “sentence”, and the input feature is extracted from the “sentence” based on the self-attention mechanism. Finally, a Multi-Layer Perception（MLP） neural network is used to predict the probability of default. The new model is trained end-to-end by the back propagation method. Experimental results show the proposed new model achieves better performance than?six other baselines on three well-known benchmark datasets.

Key words: credit scoring, self-attention mechanism, word embedding, feature extraction, deep neural network