Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring

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.

@article{marín2025_2410.10182,
  title={ Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring },
  author={ Javier Marín },
  journal={arXiv preprint arXiv:2410.10182},
  year={ 2025 }
}