用哈密顿神经网络提升信贷评分在时间外场景的稳定性
Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring
- 基于哈密顿力学设计优化器与损失函数，捕捉信用风险动态变化
- 在弗里蒙特麦克贷款数据集上，时间外预测AUC显著优于传统方法
- 模型在不同时期表现一致，适合长期金融风险评估场景
本文提出一种新型信贷评分方法，利用神经网络应对类别不平衡和时间外预测挑战。通过借鉴哈密顿力学思想，设计特定优化器与损失函数，更准确捕捉信用风险演化规律。在弗里蒙特麦克单家庭贷款层级数据集上的测试表明，该模型在时间外场景下具有更优的判别能力（AUC），且样本内与未来测试集表现一致，跨时期可靠性高。该跨学科方法融合物理系统理论与金融风险管理，为长期模型稳定性提供实用优势。
原文摘要 · Abstract (English)
This paper presents a novel credit scoring approach using neural networks to address class imbalance and out-of-time prediction challenges. We develop a specific optimizer and loss function inspired by Hamiltonian mechanics that better captures credit risk dynamics. Testing on the Freddie Mac Single-Family Loan-Level Dataset shows our model achieves superior discriminative power (AUC) in out-of-time scenarios compared to conventional methods. The approach has consistent performance between in-sample and future test sets, maintaining reliability across time periods. This interdisciplinary method spans physical systems theory and financial risk management, offering practical advantages for long-term model stability.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。