  Due to the powerful learning ability on high-rank and non-linear features,
deep neural networks (DNNs) are being applied to data mining and machine
learning in various fields, and exhibit higher discrimination performance than
conventional methods. However, the applications based on DNNs are rare in
enterprise credit rating tasks because most of DNNs employ the "end-to-end"
learning paradigm, which outputs the high-rank representations of objects and
predictive results without any explanations. Thus, users in the financial
industry cannot understand how these high-rank representations are generated,
what do they mean and what relations exist with the raw inputs. Then users
cannot determine whether the predictions provided by DNNs are reliable, and not
trust the predictions providing by such "black box" models. Therefore, in this
paper, we propose a novel network to explicitly model the enterprise credit
rating problem using DNNs and attention mechanisms. The proposed model realizes
explainable enterprise credit ratings. Experimental results obtained on
real-world enterprise datasets verify that the proposed approach achieves
higher performance than conventional methods, and provides insights into
individual rating results and the reliability of model training.
