Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring
This addresses robust credit risk prediction for financial institutions, though it appears incremental as it builds on existing neural network methods with a novel optimizer and loss function.
The paper tackles credit scoring with class imbalance and out-of-time prediction by developing a neural network approach inspired by Hamiltonian mechanics, achieving superior discriminative power (AUC) in out-of-time scenarios on the Freddie Mac dataset.
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.