{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:20:31Z","timestamp":1771003231713,"version":"3.50.1"},"reference-count":26,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p>Currently, listed companies are facing problems in tax risk management, such as lagging tax policy updates, loopholes in internal control processes, and insufficient risk identification and warning capabilities. Big data technology can track policies in real time, dig deep into massive amounts of data to accurately identify risks, strengthen internal control processes, and help enterprises effectively control tax risks. Therefore, the study builds the tax risk management evaluation system of listed firms based on the big data technology backdrop in an attempt to raise the degree of tax risk management of enterprises. The experiment adopts the comprehensive assignment to calculate the index weights, and the evaluation object is comprehensively and deeply analyzed and evaluated through fuzzy mathematical methods. The study further adopts adaptive genetic algorithm and back-propagation neural network to construct a tax risk early warning method based on big data. The experiment applied the risk evaluation method proposed by the study to a listed company to obtain a tax risk value of 3.01, which existed a higher tax risk. With a higher risk value, the risk early warning model reached convergence at the 500th time with an error of 4%. Compared to the traditional back-propagation model, the improved model had lower error in the end. The correlation coefficient of the risk early warning model was 96% with high accuracy when the tax risk value was low. The proposed tax risk management evaluation and early warning methods for listed enterprises have strong practicality and application value, which can provide more accurate TAX risk management basis for enterprises and help them control tax risk effectively.<\/jats:p>","DOI":"10.1177\/14727978251317308","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T06:00:16Z","timestamp":1739944816000},"page":"3355-3368","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Construction of tax risk management evaluation system for listed enterprises from the perspective of big data"],"prefix":"10.1177","volume":"25","author":[{"given":"Huizhi","family":"Li","sequence":"first","affiliation":[{"name":"School of Accounting, Guangzhou College of Technology and Business, Guangzhou, China"},{"name":"International Accounting Institute, Philippine Christian University, Manila, Philippine"}]},{"given":"Xianghua","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Chemistry &amp; Bio-Engineering, Hunan University of Science and Engineering, Yongzhou, China"},{"name":"Hunan Provincial Engineering Research Center for Ginkgo Biloba, Yongzhou, China"}]}],"member":"179","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1111\/itor.13257"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-022-05012-8"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1080\/09537287.2020.1810764"},{"issue":"3","key":"e_1_3_2_5_2","first-page":"636","article-title":"Forecasting value-at-risk using deep neural network quantile regression","volume":"22","author":"Chronopoulos I","year":"2024","unstructured":"Chronopoulos I, Raftapostolos A, Kapetanios G. Forecasting value-at-risk using deep neural network quantile regression. J Financ Econom 2024; 22(3): 636\u2013669.","journal-title":"J Financ Econom"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.26480\/aim.02.2022.55.61"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.4018\/JOEUC.289223"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2021.1953181"},{"issue":"1","key":"e_1_3_2_9_2","first-page":"35","article-title":"The effects of big data, artificial intelligence, and business intelligence on e-learning and business performance: evidence from Jordanian telecommunication firms","volume":"7","author":"Ahmad H","year":"2023","unstructured":"Ahmad H, Hanandeh R, Alazzawi FRY, et al. The effects of big data, artificial intelligence, and business intelligence on e-learning and business performance: evidence from Jordanian telecommunication firms. Int J Netw Sci 2023; 7(1): 35\u201340.","journal-title":"Int J Netw Sci"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/smartcities5010021"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-023-29066-8"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/app12178692"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10706-022-02273-9"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07377-0"},{"issue":"4","key":"e_1_3_2_15_2","first-page":"1","article-title":"Enterprise supply chain risk management and decision support driven by large language models","volume":"3","author":"Xu Z","year":"2024","unstructured":"Xu Z, Guo L, Zhou S, et al. Enterprise supply chain risk management and decision support driven by large language models. Applied Science and Engineering Journal for Advanced Research 2024; 3(4): 1\u20137.","journal-title":"Applied Science and Engineering Journal for Advanced Research"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/su15075882"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-023-08308-4"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2023.4902"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-021-10232-7"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10473-9"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.26480\/aim.02.2021.58.61"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11276-021-02856-z"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1108\/IMDS-11-2021-0695"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.47852\/bonviewJDSIS3202837"},{"issue":"5","key":"e_1_3_2_25_2","first-page":"1272","article-title":"Analysis of enterprise financial accounting information management from the perspective of big data","volume":"11","author":"Gao J","year":"2022","unstructured":"Gao J. Analysis of enterprise financial accounting information management from the perspective of big data. Int J Sci Res 2022; 11(5): 1272\u20131276.","journal-title":"Int J Sci Res"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1108\/ITP-01-2021-0048"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10614-021-10229-z"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978251317308","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14727978251317308","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978251317308","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:31:43Z","timestamp":1771000303000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14727978251317308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,19]]},"references-count":26,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["10.1177\/14727978251317308"],"URL":"https:\/\/doi.org\/10.1177\/14727978251317308","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,19]]}}}