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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">PBES</journal-id><journal-title-group><journal-title>Proceedings of Business and Economic Studies</journal-title></journal-title-group><issn>2209-2641</issn><eissn>2209-265X</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/pbes.v8i8.13447</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Research on the Explainable Early Warning Model and Decision-Making Path of Corporate Bond Default Risk</title><url>https://artdesignp.com/journal/PBES/8/8/10.26689/pbes.v8i8.13447</url><author>FengWeilei</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>8</volume><issue>8</issue><history><date date-type="pub"><published-time>2025-12-31</published-time></date></history><abstract>With the development of the times, the market-oriented reform of the capital market has been continuously deepened. As a key channel for direct corporate financing, the scale of the corporate bond market continues to expand. However, the frequent occurrence of default events has also significantly impacted market stability and investor interests. Traditional bond default risk early warning models often focus on improving prediction accuracy but generally suffer from the “black box” problem, making it difficult to clearly explain the formation logic of risk warning results. This greatly restricts the application value of the models in practical decision-making. In view of this, this paper analyzes the explainable early warning model and decision-making path for corporate bond default risk and proposes relevant strategies.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Černevičienė J, Kabašinskas A, 2024, Explainable Artificial Intelligence in Finance: A Systematic Literature Review. 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