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

• URL: http://arxiv.org/abs/2410.10182v2
• Date: Wed, 12 Mar 2025 06:03:20 GMT
• Title: Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring
• Authors: Javier Marín,
• Abstract summary: This paper presents a novel credit scoring approach using neural networks to address class imbalance and out-of-time prediction challenges.<n>We develop a specific and loss function inspired by Hamiltonian mechanics that better captures credit risk dynamics.
• Score: 0.5439020425819
• License: http://creativecommons.org/licenses/by/4.0/
• 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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