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

TL;DR

This paper introduces Hamiltonian Neural Networks for credit scoring, leveraging physics-inspired models to improve out-of-time prediction accuracy and stability in financial risk assessment.

Contribution

It proposes a novel neural network architecture based on Hamiltonian mechanics, specifically designed to enhance credit risk modeling over time.

Findings

Achieves higher AUC in out-of-time predictions compared to traditional models.

Maintains consistent performance between in-sample and future test sets.

Offers a physically inspired approach for more stable long-term credit scoring.

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.