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

Javier Marín

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arXiv · 2410.10182

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

Javier Marín

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Abstract

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.

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BibTeX RIS

Javier Marín. 2024-10-14. Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring. https://arxiv.org/abs/2410.10182

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