{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T08:56:07Z","timestamp":1774428967261,"version":"3.50.1"},"reference-count":20,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In the era of big data, data information has exploded, and all walks of life are impacted by big data. The arrival of big data provides the possibility for the realization of intelligent financial analysis of enterprises. At present, most enterprises\u2019 financial analysis and decision-making based on the analysis results are mainly based on human resources, with poor automation and obvious problems in efficiency and error. In order to help the senior management of enterprises to conduct scientific and effective management, the study uses big data web crawler technology and ETL technology to process data and build an intelligent financial decision support system integrating big data together with Internet plus platform. J Group in S Province is taken as an example to study the effect before and after the application of intelligent financial decision support system. The results show that the crawler technology can monitor the basic data and the big data in the industry in real time, and improve the accuracy of decision-making. Through the intelligent financial decision support system which integrates big data, the core indexes such as profit, net asset return, and accounts receivable of the enterprises can be clearly displayed. The system can query the causes of financial changes hidden behind the financial data. Through the intelligent financial decision support system, it is found that the asset liability ratio, current assets growth rate, operating income growth rate, and financial expenses of J Group are 55.27, 10.38, 20.28%, and 1,974 million RMB, respectively. The growth rate of real sales income of J Group is 0.63%, which is 31.27% less than the excellent value of the industry 31.90%. After adopting the intelligent financial decision support system, the monthly financial statements of the enterprises increase significantly, and the monthly report analysis time decreases. The maximum number of financial statements received by the Group per month is 332, and the processing time at this time is only 2\u2009h. According to the results, it can be seen that the intelligent financial decision support system integrating big data as the research result can effectively improve the financial management level of enterprises, improve the usefulness of financial decision-making, and make practical contributions to the field of corporate financial decision-making.<\/jats:p>","DOI":"10.1515\/jisys-2022-0320","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T12:20:50Z","timestamp":1694434850000},"source":"Crossref","is-referenced-by-count":9,"title":["Intelligent financial decision support system based on big data"],"prefix":"10.1515","volume":"32","author":[{"given":"Danna","family":"Tong","sequence":"first","affiliation":[{"name":"School of Finance and Tourism, Shanxi Polytechnic Institute , Xianyang 712000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guixian","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Business, Pingxiang University , Pingxiang 337000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"key":"2025120517224545358_j_jisys-2022-0320_ref_001","unstructured":"Lakshmi G, Afrin BK, Afrin F, Divya C. A study on the financial analysis of reliance industries limited. Int J Adv Res. 2021;9(5):149\u201361. 10.21474\/IJAR01\/12818."},{"key":"2025120517224545358_j_jisys-2022-0320_ref_002","unstructured":"Faria V, Andrade A, Santos J, Gasi F. Measuring the impacts of database processing utilization in innovation processes on companies. Int J Dev Res. 2020;10(3):34190."},{"key":"2025120517224545358_j_jisys-2022-0320_ref_003","doi-asserted-by":"crossref","unstructured":"Panigrahi AK, Vachhani K. Financial analysis by return on equity (ROE) and return on asset (ROA)-A comparative study of HUL and ITC. J Manag Res Anal. 2021;8(3):131\u20138. 10.18231\/j.jmra.2021.027.","DOI":"10.18231\/j.jmra.2021.027"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_004","doi-asserted-by":"crossref","unstructured":"Ma Y. Feasibility study on listing of pharmaceutical enterprises based on financial analysis: Take company Y as an example. Open J Bus Manag. 2021;9(3):1325\u201337. 10.4236\/ojbm.2021.93072.","DOI":"10.4236\/ojbm.2021.93072"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_005","doi-asserted-by":"crossref","unstructured":"Deng C, Li G, Zhou Q, Li J. Guarantee the quality-of-service of control transactions in real-time database systems. IEEE Access. 2020;8(1):110511\u201322. 10.1109\/ACCESS.2020.3002335.","DOI":"10.1109\/ACCESS.2020.3002335"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_006","doi-asserted-by":"crossref","unstructured":"Qi E, Deng M. R&D investment enhance the financial performance of company driven by big data computing and analysis. Comput Syst Sci Eng. 2019;34(4):237\u201348. 10.32604\/csse.2019.34.237.","DOI":"10.32604\/csse.2019.34.237"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_007","doi-asserted-by":"crossref","unstructured":"Jin H, Luo L, Wang X, Zhu X, Qian L, Zhang Z. Financial credit default forecast based on big data analysis. Vol. 3, No. 8. Francis Academic Press; 2021. p. 51\u20136. 10.25236\/AJBM.2021.030810.","DOI":"10.25236\/AJBM.2021.030810"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_008","doi-asserted-by":"crossref","unstructured":"Zhu X, Yang Y. Big data analytics for improving financial performance and sustainability. J Syst Sci Inf. 2021;9(2):175\u201391. 10.21078\/JSSI-2021-175-17.","DOI":"10.21078\/JSSI-2021-175-17"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_009","doi-asserted-by":"crossref","unstructured":"Tavera Romero CA, Ortiz JH, Khalaf OI, Prado AR. Web application commercial design for financial entities based on business intelligence. Comput Mater Contin. 2021;67(3):3177\u201388. 10.32604\/cmc.2021.014738.","DOI":"10.32604\/cmc.2021.014738"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_010","doi-asserted-by":"crossref","unstructured":"Xiao F, Ke J. Pricing, management and decision making of financial markets with artificial intelligence: Introduction to the issue. Financ Innov. 2021;7(1):1757\u20139. 10.1186\/s40854-021-00302-9.","DOI":"10.1186\/s40854-021-00302-9"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_011","doi-asserted-by":"crossref","unstructured":"Yun U, Nam H, Kim J, Kim H, Baek Y, Lee J, et al. Efficient transaction deleting approach of pre-large based high utility pattern mining in dynamic databases. Future Gener Comput Syst. 2020;103(1):58\u201378. 10.1016\/j.future.2019.09.024.","DOI":"10.1016\/j.future.2019.09.024"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_012","doi-asserted-by":"crossref","unstructured":"Cuzzocrea A, Karras P, Vlachou A. Effective and efficient skyline query processing over attribute-order-preserving-free encrypted data in cloud-enabled databases. Future Gener Comput Syst. 2022;126(1):237\u201351. 10.1016\/j.future.2021.08.008.","DOI":"10.1016\/j.future.2021.08.008"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_013","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez GG, Gonzalez-Cava JM, P\u00e9rez JAM. An intelligent decision support system for production planning based on machine learning. J Intell Manuf. 2020;31(5):1257\u201373. 10.1007\/s10845-019-01510-y.","DOI":"10.1007\/s10845-019-01510-y"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_014","doi-asserted-by":"crossref","unstructured":"Geng C, Xu Y, Metawa N. Intelligent financial decision support system based on data mining. J Intell Fuzzy Syst. 2021;(2):1\u201310. 10.3233\/JIFS-189838.","DOI":"10.3233\/JIFS-189838"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_015","unstructured":"Xian X, Liu J. Application of chaos theory in incomplete randomized financial analysis. Sci Publ Group. 2019;6(6):306\u201310."},{"key":"2025120517224545358_j_jisys-2022-0320_ref_016","unstructured":"Kang Q. Financial risk assessment model based on big data. Int J Model Simul Sci Comput. 2019;(4):38\u201351. 10.1142\/S1793962319500211."},{"key":"2025120517224545358_j_jisys-2022-0320_ref_017","doi-asserted-by":"crossref","unstructured":"Jung K, Kim D, Yu S. Next generation models for portfolio risk management: An approach using financial big data. J Risk Insur. 2022;89(3):765\u201387. 10.1111\/jori.12374.","DOI":"10.1111\/jori.12374"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_018","doi-asserted-by":"crossref","unstructured":"Yang A. Analysis on internet financial business and construction of credit system. Proc Bus Econ Stud. 2020;3(2):1\u20134. 10.26689\/pbes.v3i2.1164.","DOI":"10.26689\/pbes.v3i2.1164"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_019","doi-asserted-by":"crossref","unstructured":"Liu H, Sun G. Research on the application of big data technology in the integration of enterprise business and finance. J Big Data. 2021;3(4):175\u201382. 10.32604\/jbd.2021.024074.","DOI":"10.32604\/jbd.2021.024074"},{"key":"2025120517224545358_j_jisys-2022-0320_ref_020","doi-asserted-by":"crossref","unstructured":"Tang Y. Big data analytics of taxi operations in New York City. Am J Oper Res. 2019;9(4):192\u20139. 10.4236\/ajor.2019.94012.","DOI":"10.4236\/ajor.2019.94012"}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2022-0320\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2022-0320\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T17:24:37Z","timestamp":1764955477000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2022-0320\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,1]]},"references-count":20,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,6,23]]},"published-print":{"date-parts":[[2023,6,23]]}},"alternative-id":["10.1515\/jisys-2022-0320"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2022-0320","relation":{},"ISSN":["2191-026X"],"issn-type":[{"value":"2191-026X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,1]]},"article-number":"20220320"}}