{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:47:41Z","timestamp":1765889261936,"version":"3.38.0"},"reference-count":25,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,2,20]]},"abstract":"<jats:p>A financial risk early warning system (FREWS) is a disclosure and tracking mechanism that provides advance notice of potential issues, hazards, and potentials that might affect the business\u2019s finances. Some elderly individuals living alone may experience financial difficulties, which may hinder their ability to pay for appropriate medical care, property maintenance, and other essential expenses. Financial difficulties can add tension and diminish their quality of life. Financial results, investment risk, and possible insolvencies may all be detected by implementing early warning systems. Management might use the window of opportunity provided by early warning systems to avert or lessen the impact of possible issues. Almost all FREWS rely on some financial statement analysis. Financial measures are combined with the EWS, accounting information, to determine the firm\u2019s success in its field. Organizational success depends on effective financial oversight, which is at the heart of each business. Studying the enhancement of early warning capacities is relevant because there are no adequate risk evaluation methods to generate realistic estimates. To minimize the FREWS, this research provides a systemic model based on a second-order block chain differential equation (SBDE). China\u2019s systemic financial liabilities have also been quantified using the expected investment returns of 64\u00a0selected financial enterprises in China between February 2006 and September 2020 as the datasets. The financial risk warning approach is compared and analyzed primarily using analytical and comparative techniques. The suggested method is 96% accurate in experiments. Consequently, the proposed algorithm compares favorably to others regarding both computing efficacy and precision and has strong predictability.<\/jats:p>","DOI":"10.3233\/idt-230318","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T16:17:08Z","timestamp":1708445828000},"page":"327-342","source":"Crossref","is-referenced-by-count":6,"title":["Research on financial risk early warning system model based on second-order blockchain differential equation"],"prefix":"10.1177","volume":"18","author":[{"given":"Hongyan","family":"Li","sequence":"first","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/IDT-230318_ref1","doi-asserted-by":"crossref","first-page":"113260","DOI":"10.1016\/j.cam.2020.113260","article-title":"Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment","volume":"386","author":"Du","year":"2021","journal-title":"Journal of Computational and Applied Mathematics"},{"key":"10.3233\/IDT-230318_ref2","doi-asserted-by":"crossref","first-page":"101383","DOI":"10.1016\/j.najef.2021.101383","article-title":"Systemic financial risk early warning of the financial market in China using Attention-LSTM model","volume":"56","author":"Ouyang","year":"2021","journal-title":"The North American Journal of Economics and Finance"},{"key":"10.3233\/IDT-230318_ref3","doi-asserted-by":"crossref","first-page":"104119","DOI":"10.1016\/j.jedc.2021.104119","article-title":"News and narratives in financial systems: exploiting big data for systemic risk assessment","volume":"127","author":"Nyman","year":"2021","journal-title":"Journal of Economic Dynamics and Control"},{"issue":"5","key":"10.3233\/IDT-230318_ref4","doi-asserted-by":"crossref","first-page":"716","DOI":"10.3846\/tede.2019.8740","article-title":"Machine learning methods for systemic risk analysis in financial sectors","volume":"25","author":"Kou","year":"2019","journal-title":"Technological and Economic Development of Economy"},{"key":"10.3233\/IDT-230318_ref5","unstructured":"DeFries RS, Edenhofer O, Halliday AN, Heal GM, Lenton T, Puma M, Rising J, Rockstr\u00f6m J, Ruane A, Schellnhuber HJ, Stainforth D. 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A convolutional neural network-based model for supply chain financial risk early warning. 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Design and Implementation of China Financial Risk Monitoring and Early Warning System Based on Deep Learning. 2023; IEEE Access.","DOI":"10.1109\/ACCESS.2023.3280934"},{"issue":"2","key":"10.3233\/IDT-230318_ref25","doi-asserted-by":"crossref","first-page":"364","DOI":"10.3846\/jbem.2022.16065","article-title":"Nonlinear spillover effect of US monetary policy uncertainty on China\u2019s systematic financial risks","volume":"23","author":"Ouyang","year":"2022","journal-title":"Journal of Business Economics and Management"}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-230318","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T08:33:21Z","timestamp":1741682001000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-230318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,20]]},"references-count":25,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/idt-230318","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"type":"print","value":"1872-4981"},{"type":"electronic","value":"1875-8843"}],"subject":[],"published":{"date-parts":[[2024,2,20]]}}}