A Generative Approach to Credit Prediction with Learnable Prompts for Multi-scale Temporal Representation Learning

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. In extensive evaluations, FinLangNet significantly outperforms a production XGBoost system, achieving a 7.2% improvement in the KS metric and a 9.9% relative reduction in bad debt rate. Its effectiveness as a general-purpose sequential learning framework is further validated through state-of-the-art performance on the public UEA time series classification benchmark. The system has been successfully deployed on DiDi's international finance platform, serving leading financial credit companies in Latin America.