通过双粒度提示构建异质金融数据建模的统一框架
A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting
AI总结：
本文提出FinLangNet框架，通过双模块架构结合表格特征提取与时间序列建模，解决工业信用评分中异质金融数据复杂性和信用worthiness演变的挑战，实验证明其在KS指标和坏账率上有显著提升。
摘要：
近期工业信用评分模型仍严重依赖手动调优的统计学习方法。尽管具有潜力，深度学习架构在工业信用评分中难以持续超越传统统计模型，主要由于异质金融数据的复杂性和信用worthiness演变的建模挑战。为此，我们引入FinLangNet，一种将信用评分重新表述为多尺度序列学习问题的新框架。FinLangNet通过双模块架构处理异质金融数据，结合表格特征提取与时间序列建模，生成用户未来财务行为的多时间跨度概率分布。关键创新是序列模块中的双提示机制，引入在特征级粒度和用户级粒度上操作的可学习提示。值得注意的是，实际世界部署实现了KS指标的6.3个百分点提升，以及坏账率的9.9%下降。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
