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

Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2025 V1 Latest version Share on Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring Authors : Javier Marin Valenzuela 0000-0003-0957-1283 [email protected] and Javier Marín Authors Info & Affiliations https://doi.org/10.22541/au.173645377.76227896/v1 194 views 95 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper introduces a novel Hamiltonian-inspired neural network approach to credit scoring, designed to address the challenges of class imbalance and out-of-time (OOT) prediction in financial risk management. Drawing from concepts in Hamiltonian mechanics, we develop a symplectic optimizer and a new loss function to capture the complex dynamics of credit risk evolution. Using the Freddie Mac Single-Family Loan-Level Dataset, we evaluate our model's performance against other machine learning approaches. Our method shows superior discriminative power in OOT scenarios, as measured by the Area Under the Curve (AUC), indicating better ranking ability and robustness to class imbalance. The Hamiltonian-inspired approach shows particular strength in maintaining consistent performance between in-sample and OOT test sets, suggesting improved generalization to future, unseen data. These findings suggest that physics-inspired techniques offer a promising direction for developing more robust and reliable credit scoring models, particularly in uncertain economic situations. Supplementary Material File (hamiltonian neural networks for robust out-of-time credit scoring.pdf) Download 278.03 KB Information & Authors Information Version history V1 Version 1 09 January 2025 Copyright This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License Keywords class imbalance credit scoring hamiltonian neural networks out-of-time prediction Authors Affiliations Javier Marin Valenzuela 0000-0003-0957-1283 [email protected] View all articles by this author Javier Marín View all articles by this author Metrics & Citations Metrics Article Usage 194 views 95 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Javier Marin Valenzuela, Javier Marín. Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring. Authorea . 09 January 2025. DOI: https://doi.org/10.22541/au.173645377.76227896/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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