Explainable Gradient Boosting for Corporate Crisis Forecasting in Italian Businesses

Explainable Gradient Boosting for Corporate Crisis Forecasting in Italian Businesses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Explainable Gradient Boosting for Corporate Crisis Forecasting in Italian Businesses Donato Riccio, Fabrizio Maturo, Andrea Mazzitelli, Giuseppe Bifulco, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4426436/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Scholars have taken a keen interest in predicting corporate crises in the past decades. However, most studies focused on classical parametric models that, by their nature, can consider few predictors and interactions and must respect numerous assumptions. Over the past few years, the economy has faced a severe structural crisis that has resulted in significantly lower income, cash, and capital levels than in the past. This crisis has led to insolvency and bankruptcy in many cases. Hence, there is a renewed interest in research for new models for forecasting business crises using novel advanced machine learning techniques. This study aims to develop a model that achieves state-of-the-art accuracy while being fully interpretable, overcoming the limitations of previous research. The model demonstrates excellent predictive performance on par with black-box approaches while maintaining complete transparency by leveraging Explainable Boosting Machines, an intrinsically interpretable tree-based ensemble method, and hyperparameter optimization. The approach automatically considers all possible interactions and uncovers relevant aspects not considered in past studies. This line of research provides compelling results that can bring new insights to the literature on corporate crisis prediction. The interpretable nature of the model is a key advancement, enabling practical application and a deeper understanding of the factors driving corporate financial distress. Applied Statistics Artificial Intelligence and Machine Learning corporate crises financial distress machine learning explainable AI Explainable Gradient Boosting classification Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4426436","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":302904633,"identity":"382d398a-bd91-4796-a7d1-a455290a18db","order_by":0,"name":"Donato Riccio","email":"","orcid":"https://orcid.org/0009-0001-2147-7831","institution":"Machine Learning Engineer, graduated with honours in Data Analytics and currently pursuing a Master's degree in Data Science at the University of Campania Luigi Vanvitelli, Caserta, 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