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This cascading effect can transform isolated financial distress into systemic risk. Therefore, the prediction of firms\u2019 defaults from a comprehensive perspective is vital for ensuring the long-term stability of economic growth. To address this challenge, we propose a temporal graph learning framework that integrates macroeconomic trends and a tailored risk matrix, capturing both external shocks and internal contagion risks. Validated on a large commercial bank dataset with a total loan volume of USD 3 trillion, our system risk dynamic prediction (SRDP) method achieves 88.3% area under curve (AUC), considerably outperforming the state-of-the-art baselines. Risk mitigation analysis reveals that targeting the top 1% of predicted high-risk nodes reduces systemic risk exposure by an average of 25%, with heightened efficacy during financial crises and market volatility phases. The proposed method empowers financial regulators and risk managers with enhanced capabilities to monitor and grasp risks embedded within networked-loans against systemic financial crises.<\/jats:p>","DOI":"10.34133\/icomputing.0193","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T05:41:16Z","timestamp":1757482876000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":1,"title":["Temporal Graph Learning for Default Prediction and Systemic Risk Mitigation in Financial Networks"],"prefix":"10.34133","volume":"4","author":[{"given":"Moyang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Intelligence and Computing, \rTianjin University, Tianjin, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taijun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Intelligence and Computing, \rTianjin University, Tianjin, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junqi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Intelligence and Computing, \rTianjin University, Tianjin, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5171-7648","authenticated-orcid":true,"given":"Zhibin","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Intelligence and Computing, \rTianjin University, Tianjin, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Intelligence and Computing, \rTianjin University, Tianjin, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"2693","DOI":"10.1038\/s41467-020-16535-8","article-title":"Evolution of the Chinese guarantee network under financial crisis and stimulus program","volume":"11","author":"Wang Y","year":"2020","unstructured":"Wang Y, Zhang Q, Yang X. 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