  Recent industrial applications in risk prediction still heavily rely on
extensively manually-tuned, statistical learning methods. Real-world financial
data, characterized by its high dimensionality, sparsity, high noise levels,
and significant imbalance, poses unique challenges for the effective
application of deep neural network models. In this work, we introduce a novel
deep learning risk prediction framework, FinLangNet, which conceptualizes
credit loan trajectories in a structure that mirrors linguistic constructs.
This framework is tailored for credit risk prediction using real-world
financial data, drawing on structural similarities to language by adapting
natural language processing techniques. It particularly emphasizes analyzing
the development and forecastability of mid-term credit histories through
multi-head and sequences of detailed financial events. Our research
demonstrates that FinLangNet surpasses traditional statistical methods in
predicting credit risk and that its integration with these methods enhances
credit overdue prediction models, achieving a significant improvement of over
4.24\% in the Kolmogorov-Smirnov metric.
