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

Abstract

Recent industrial credit scoring models remain heavily reliant on manually tuned statistical learning methods. While deep learning offers promising solutions, its effectiveness is often limited by the complexity of financial data, particularly in long-horizon scenarios. In this work, we propose FinLangNet, which addresses credit scoring by reframing it as the task of generating multi-scale distributions of a user's future behavior. Within this framework, tabular data is transformed into sequential representations, enabling the generation of user embeddings across multiple temporal scales. Inspired by the recent success of prompt-based training in Large Language Models (LLMs), FinLangNet also introduces two types of prompts to model and capture user behavior at both the feature-granularity and user-granularity levels. Experimental results demonstrate that FinLangNet outperforms the online XGBoost benchmark, achieving a 7.2\% improvement in KS metric performance and a 9.9\% reduction in the relative bad debt rate. Furthermore, FinLangNet exhibits superior performance on public UEA archives, underscoring its scalability and adaptability in time series classification tasks.

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Reproductions