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

Javier Marín

TL;DR

The paper tackles the challenge of time-varying credit risk and concept drift by developing a Hamiltonian-inspired neural optimization framework that integrates physics-based dynamics (symplectic updates and energy normalization) with a loss that regularizes model parameters. Evaluated on the Freddie Mac SFLLD via out-of-time splits, the approach demonstrates superior AUC and temporal stability compared to a strong baseline like XGBoost, albeit at a cost to conventional accuracy metrics. The work highlights the potential of physics-inspired optimization to improve long-horizon, robust discrimination in credit scoring, and suggests avenues for hybrid models and improved interpretability to enhance practical adoption in regulated financial settings.

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.

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

TL;DR

The paper tackles the challenge of time-varying credit risk and concept drift by developing a Hamiltonian-inspired neural optimization framework that integrates physics-based dynamics (symplectic updates and energy normalization) with a loss that regularizes model parameters. Evaluated on the Freddie Mac SFLLD via out-of-time splits, the approach demonstrates superior AUC and temporal stability compared to a strong baseline like XGBoost, albeit at a cost to conventional accuracy metrics. The work highlights the potential of physics-inspired optimization to improve long-horizon, robust discrimination in credit scoring, and suggests avenues for hybrid models and improved interpretability to enhance practical adoption in regulated financial settings.

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.

Paper Structure

This paper contains 5 sections, 6 equations, 1 figure, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Hamiltonian Mechanics in Neural Networks optimization
  3. Data and Methodology
  4. Results and discussion
  5. Conclusions

Figures (1)

  • Figure 1: Equivalence between Hamiltonian systems in physical systems, and its application in NN optimization spaces. Position $q$ is equivalent to the NN parameters $\theta$, and momentum vector $p$ is equivalent to the difference between consecutive parameters states.