Recent industrial credit scoring models remain heavily reliant on manually tuned statistical learning methods. Despite their potential, deep learning architectures have struggled to consistently outperform traditional statistical models in industrial credit scoring, largely due to the complexity of heterogeneous financial data and the challenge of modeling evolving creditworthiness. To bridge this gap, we introduce FinLangNet, a novel framework that reformulates credit scoring as a multi-scale sequential learning problem. FinLangNet processes heterogeneous financial data through a dual-module architecture that combines tabular feature extraction with temporal sequence modeling, generating probability distributions of users' future financial behaviors across multiple time horizons. A key innovation is our dual-prompt mechanism within the sequential module, which introduces learnable prompts operating at both feature-level granularity for capturing fine-grained temporal patterns and user-level granularity for aggregating holistic risk profiles. Notably, real world deployment yielded a 6.3 pp improvement in KS, along with a 9.9\% reduction in bad debt rate.

翻译：近期工业信用评分模型仍然严重依赖人工调优的统计学习方法。尽管深度学习架构具有潜力，但由于异构金融数据的复杂性以及不断演变的信用度建模挑战，其在工业信用评分中始终难以持续超越传统统计模型。为弥合这一差距，我们提出FinLangNet，一个将信用评分重新表述为多尺度序列学习问题的新颖框架。FinLangNet通过结合表格特征提取与时间序列建模的双模块架构处理异构金融数据，生成用户在多个时间跨度上未来金融行为的概率分布。其关键创新在于序列模块内的双提示机制，该机制在特征级粒度（用于捕获细粒度时间模式）和用户级粒度（用于聚合整体风险画像）上引入可学习的提示。值得注意的是，在实际部署中，KS值提升了6.3个百分点，同时坏账率降低了9.9%。