Skip to main content
SpringerLink
Log in

Enhancing Efficiency and Decision-Making in Higher Education Through Intelligent Commercial Integration: Leveraging Artificial Intelligence

  • Published:
Journal of the Knowledge Economy Aims and scope Submit manuscript

Abstract

The integration of artificial intelligence (AI) into financial management processes within academic institutions has ushered in a transformative era. This research paper delves into the profound implications of AI-driven financial integration, emphasizing its significance in aligning academic financial service functions with greater cohesion and efficiency. The establishment of intelligent financial systems, underpinned by meticulous data management and AI capabilities, has redefined the landscape of financial management in colleges and universities. This study explores the intricate relationship between AI-driven financial integration and its impact on job responsibilities and financial positions. It uncovers gaps in existing research and formulates pertinent questions to deepen our understanding of the integration process between industry and finance in the intelligent era. The research revolves around constructing a three-level financial management intelligent system, encompassing fine management, cost-effective operations, and enhancing links in financial processes. The findings underscore the transformative potential of AI in streamlining financial operations, emphasizing its role in liberating financial personnel from routine tasks. This paradigm shift not only streamlines financial operations but also augments financial productivity, unlocking the full potential of financial management within academic institutions. Theoretical implications highlight the need for ongoing theoretical development to accommodate the evolving role of AI in financial ecosystems. Managerial implications advocate for the strategic adoption of AI-driven financial platforms, fostering a culture of creativity and strategic contributions. Proactive managerial involvement in AI adoption can yield substantial benefits, requiring a nuanced approach to organizational change management and continuous innovation. This research paper paves the way for a more intelligent and integrated future in academic financial management, with AI driving efficiency and adaptability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

The experimental data used to support the findings of this study are available from the corresponding author upon request.

References

  • Abubakar, A. I., Omeke, K. G., Ozturk, M., Hussain, S., & Imran, M. A. (2020). The role of artificial intelligence driven 5G networks in COVID-19 outbreak: Opportunities, challenges, and future outlook. Frontiers in Communications and Networks, 1, 575065.

    Article  Google Scholar 

  • Aho, B., & Duffield, R. (2020). Beyond surveillance capitalism: Privacy, regulation and big data in Europe and China. Economy and Society, 49(2), 187–212.

    Article  Google Scholar 

  • Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2023). Rethinking data strategy and integration for artificial intelligence: Concepts, opportunities, and challenges. Applied Sciences, 13(12), 7082.

    Article  Google Scholar 

  • Allioui, H., & Mourdi, Y. (2023). Exploring the full potentials of IoT for better financial growth and stability: A comprehensive survey. Sensors, 23(19), 8015.

    Article  Google Scholar 

  • Amini, M., & Abukari, A. M. (2020). ERP systems architecture for the modern age: A review of the state of the art technologies. Journal of Applied Intelligent Systems and Information Sciences, 1(2), 70–90.

    Google Scholar 

  • Amoako, G., Omari, P., Kumi, D. K., Agbemabiase, G. C., & Asamoah, G. (2021). Conceptual framework—artificial intelligence and better entrepreneurial decision-making: The influence of customer preference, industry benchmark, and employee involvement in an emerging market. Journal of Risk and Financial Management, 14(12), 604.

    Article  Google Scholar 

  • Arnarsson Í Ö.(2020). Systematic Analysis of Engineering Change Request Data: Applying Data Mining Tools to Gain New Fact-Based Insights. Chalmers Tekniska Hogskola (Sweden), 2, 14–22.

  • Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58, 82–115.

    Article  Google Scholar 

  • Asatiani, A., Malo, P., Nagbøl, P. R., Penttinen, E., Rinta-Kahila, T., & Salovaara, A. (2021). Sociotechnical envelopment of artificial intelligence: An approach to organizational deployment of inscrutable artificial intelligence systems. Journal of the Association for Information Systems (JAIS), 22(2), 325–352.

    Article  Google Scholar 

  • Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021). Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications, 175, 114820.

    Article  Google Scholar 

  • Bodkhe, U., Tanwar, S., Parekh, K., Khanpara, P., Tyagi, S., Kumar, N., & Alazab, M. (2020). Blockchain for Industry 4.0: A comprehensive review. IEEE Access, 8, 79764–79800.

    Article  Google Scholar 

  • Boulos, M. N. K. (2004). Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom. International Journal of Health Geographics, 3, 1–50.

    Article  Google Scholar 

  • Boute, R. N., Gijsbrechts, J., & Van Mieghem, J. A. (2022). Digital lean operations: Smart automation and artificial intelligence in financial services. Innovative Technology at the Interface of Finance and Operations:, I, 175–188.

    Article  Google Scholar 

  • Chaudhry, M., Shafi, I., Mahnoor, M., Vargas, D. L. R., Thompson, E. B., & Ashraf, I. (2023). A systematic literature review on identifying patterns using unsupervised clustering algorithms: A data mining perspective. Symmetry, 15(9), 1679.

    Article  Google Scholar 

  • Chen, Z. (2019). How the choice of reference group matters: Economic integration of rural-to-urban migrants in China. Journal of Ethnic and Migration Studies, 47(19), 4428–4456.

    Article  Google Scholar 

  • Chigbu, B. I., & Nekhwevha, F. H. (2021). The future of work and uncertain labour alternatives as we live through the industrial age of possible singularity: Evidence from South Africa. Technology in Society, 67, 101715.

    Article  Google Scholar 

  • Das Swain, V., Saha, K., Rajvanshy, H., Sirigiri, A., Gregg, J. M., Lin, S., & De Choudhury, M. (2019). A multisensor person-centered approach to understand the role of daily activities in job performance with organizational personas. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(4), 1–27.

    Article  Google Scholar 

  • Davis, G. F. (2009). Managed by the markets: How finance re-shaped America. OUP Oxford.

    Google Scholar 

  • De Villiers, R. (2021). Seven principles to ensure future-ready accounting graduates–A model for future research and practice. Meditari Accountancy Research, 29(6), 1354–1380.

    Article  Google Scholar 

  • Debbarma, R. (2023). The changing landscape of privacy laws in the age of big data and surveillance. Rivista Italiana Di Filosofia Analitica Junior, 14(2), 1740–1752.

    Google Scholar 

  • Dhar, B. K., Sarkar, S. M., & Ayittey, F. K. (2022). Impact of social responsibility disclosure between implementation of green accounting and sustainable development: A study on heavily polluting companies in Bangladesh. Corporate Social Responsibility and Environmental Management, 29(1), 71–78.

    Article  Google Scholar 

  • Dunis, C., Middleton, P. W., Karathanasopolous, A., & Theofilatos, K. (2016). Artificial intelligence in financial markets. Palgrave Macmillan.

    Book  Google Scholar 

  • Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.

    Article  Google Scholar 

  • Firouzi, F., Farahani, B., Daneshmand, M., Grise, K., Song, J., Saracco, R., & Luo, A. (2021). Harnessing the power of smart and connected health to tackle COVID-19: IoT, AI, robotics, and blockchain for a better world. IEEE Internet of Things Journal, 8(16), 12826–12846.

    Article  Google Scholar 

  • Fu, Q., Abdul Rahman, A. A., Jiang, H., Abbas, J., & Comite, U. (2022). Sustainable supply chain and business performance: The impact of strategy, network design, information systems, and organizational structure. Sustainability, 14(3), 1080.

    Article  Google Scholar 

  • Gieure, C., del Mar Benavides-Espinosa, M., & Roig-Dobón, S. (2020). The entrepreneurial process: The link between intentions and behavior. Journal of Business Research, 112, 541–548.

    Article  Google Scholar 

  • Girasa, R. (2020). Artificial intelligence as a disruptive technology: Economic transformation and government regulation. Springer Nature.

    Book  Google Scholar 

  • Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of Management Information Systems, 35(1), 220–265.

    Article  Google Scholar 

  • Goswami, M., & Daultani, Y. (2022). Make-in-India and Industry 40: Technology readiness of select firms, barriers and socio-technical implications. The TQM Journal, 34(6), 1485–1505.

    Article  Google Scholar 

  • Gunduz, M. Z., & Das, R. (2020). Cyber-security on smart grid: Threats and potential solutions. Computer Networks, 169, 107094.

    Article  Google Scholar 

  • Gupta, S., & Kumar, M. (2020). Forensic document examination system using boosting and bagging methodologies. Soft Computing, 24, 5409–5426.

    Article  Google Scholar 

  • Hameed, K., Barika, M., Garg, S., Amin, M. B., & Kang, B. (2022). A taxonomy study on securing blockchain-based Industrial applications: An overview, application perspectives, requirements, attacks, countermeasures, and open issues. Journal of Industrial Information Integration, 26, 100312.

    Article  Google Scholar 

  • He, Y., Zhou, F., Qi, M., & Wang, X. (2020). Joint distribution: Service paradigm, key technologies and its application in the context of Chinese express industry. International Journal of Logistics Research and Applications, 23(3), 211–227.

    Article  Google Scholar 

  • He, H., Gray, J., Cangelosi, A., Meng, Q., McGinnity, T. M., & Mehnen, J. (2021). The challenges and opportunities of human-centered AI for trustworthy robots and autonomous systems. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1398–1412.

    Article  Google Scholar 

  • Huang, M., Bao, Q., Zhang, Y., & Feng, W. (2019). A hybrid algorithm for forecasting financial time series data based on DBSCAN and SVR. Information, 10(3), 103.

    Article  Google Scholar 

  • Hurst, W., Boddy, A., Merabti, M., & Shone, N. (2020). Patient privacy violation detection in healthcare critical infrastructures: An investigation using density-based benchmarking. Future Internet, 12(6), 100.

    Article  Google Scholar 

  • Ismagilova, E., Hughes, L., Rana, N. P., & Dwivedi, Y. K. (2020). Security, privacy and risks within smart cities: Literature review and development of a smart city interaction framework. Information Systems Frontiers, 1–22.

  • Johannessen, L. E. (2019). Negotiated discretion: Redressing the neglect of negotiation in ‘street-level bureaucracy.’ Symbolic Interaction, 42(4), 513–538.

    Article  Google Scholar 

  • John, J. M., Shobayo, O., & Ogunleye, B. (2023). An exploration of clustering algorithms for customer segmentation in the UK retail market. Analytics, 2(4), 809–823.

    Article  Google Scholar 

  • Kaber, D. B., Onal, E., & Endsley, M. R. (2000). Design of automation for telerobots and the effect on performance, operator situation awareness, and subjective workload. Human Factors and Ergonomics in Manufacturing & Service Industries, 10(4), 409–430.

    Article  Google Scholar 

  • Karajovic, M., Kim, H. M., & Laskowski, M. (2019). Thinking outside the block: Projected phases of blockchain integration in the accounting industry. Australian Accounting Review, 29(2), 319–330.

    Article  Google Scholar 

  • Khanna, N. N., Maindarkar, M. A., Viswanathan, V., Fernandes, J. F. E., Paul, S., Bhagawati, M., Suri, J. S., et al. (2022). Economics of artificial intelligence in healthcare: Diagnosis vs. treatment. Healthcare (p. 2493). MDPI.

    Google Scholar 

  • Kommunuri, J. (2022). Artificial intelligence and the changing landscape of accounting: A viewpoint. Pacific Accounting Review, 34(4), 585–594.

    Article  Google Scholar 

  • Kriswardhana, W., & Esztergár-Kiss, D. (2023). Exploring the aspects of MaaS adoption based on college students’ preferences. Transport Policy, 136, 113–125.

    Article  Google Scholar 

  • Lee, C. T., & Pan, L. Y. (2023). Smile to pay: Predicting continuous usage intention toward contactless payment services in the post-COVID-19 era. International Journal of Bank Marketing, 41(2), 312–332.

    Article  Google Scholar 

  • Li, J., & Kassem, M. (2021). Applications of distributed ledger technology (DLT) and blockchain-enabled smart contracts in construction. Automation in Construction, 132, 103955.

    Article  Google Scholar 

  • Li, J., Herdem, M. S., Nathwani, J., & Wen, J. Z. (2023). Methods and applications for artificial intelligence, big data, internet of things, and blockchain in smart energy management. Energy and AI, 11, 100208.

    Article  Google Scholar 

  • Liang, T. P., & Liu, Y. H. (2018). Research landscape of business intelligence and big data analytics: A bibliometrics study. Expert Systems with Applications, 111, 2–10.

    Article  Google Scholar 

  • Liu, C., Feng, Y., Lin, D., Wu, L., & Guo, M. (2020). IoT based laundry services: An application of big data analytics, intelligent logistics management, and machine learning techniques. International Journal of Production Research, 58(17), 5113–5131.

    Article  Google Scholar 

  • Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1–29.

    Article  Google Scholar 

  • Luca, M., & Bazerman, M. H. (2021). The power of experiments: Decision making in a data-driven world. MIT Press.

    Google Scholar 

  • Lyu, T., Guo, Y., & Chen, H. (2023). Understanding people’s intention to use facial recognition services: The roles of network externality and privacy cynicism. Information Technology & People, ahead-of-print.

  • Minkkinen, M., Niukkanen, A., & Mäntymäki, M. (2022). What about investors? ESG analyses as tools for ethics-based AI auditing. AI & Society, 39(1), 329–343.

  • Moll, J., & Yigitbasioglu, O. (2019). The role of internet-related technologies in shaping the work of accountants: New directions for accounting research. The British Accounting Review, 51(6), 100833.

    Article  Google Scholar 

  • Morales, J., Lemmen, C., de By, R. A., Dávila, A. E. O., & Molendijk, M. (2021). Designing all-inclusive land administration systems: A case study from Colombia. Land Use Policy, 109, 105617.

    Article  Google Scholar 

  • Moselhi, O., Bardareh, H., & Zhu, Z. (2020). Automated data acquisition in construction with remote sensing technologies. Applied Sciences, 10(8), 2846.

    Article  Google Scholar 

  • Nabukeera, J., Amutuhaire, V., Nassiwa, E., & Nazze, H. (2023). Design and development of an electronic filing system for the management of administrative records at National Women’s Council (Doctoral dissertation, Makerere University), 31–40.

  • Netinant, P., Saengsuwan, N., Rukhiran, M., & Pukdesree, S. (2023). Enhancing data management strategies with a hybrid layering framework in assessing data validation and high availability sustainability. Sustainability, 15(20), 15034.

    Article  Google Scholar 

  • Nguyen, D. K., Sermpinis, G., & Stasinakis, C. (2023). Big data, artificial intelligence and machine learning: A transformative symbiosis in favour of financial technology. European Financial Management, 29(2), 517–548.

    Article  Google Scholar 

  • Ogie, R. I., Perez, P., & Dignum, V. (2017). Smart infrastructure: An emerging frontier for multidisciplinary research. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 170(1), 8–16.

    Article  Google Scholar 

  • Ohlhorst, F. J. (2012). Big data analytics: turning big data into big money (Vol. 65). John Wiley & Sons.

  • Ou, S. M. (2020). Exploring crucial factors for integrating medical cloud and healthcare logistics by DANP. International Journal of Engineering Science Technologies, 4(1), 11–18.

    Article  Google Scholar 

  • Oviatt, S., & Cohen, P. R. (2022). The paradigm shift to multimodality in contemporary computer interfaces. Springer Nature.

    Google Scholar 

  • Pal, D., Vanijja, V., & Papasratorn, B. (2015). An empirical analysis towards the adoption of NFC mobile payment system by the end user. Procedia Computer Science, 69, 13–25.

    Article  Google Scholar 

  • Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Peru Ministerio de Education. https://hdl.handle.net/20.500.12799/6533.

  • Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2020). Big data and AI–A transformational shift for government: So, what next for research? Public Policy and Administration, 35(1), 24–44.

    Article  Google Scholar 

  • Pramanik, H. S., Kirtania, M., & Pani, A. K. (2019). Essence of digital transformation—Manifestations at large financial institutions from North America. Future Generation Computer Systems, 95, 323–343.

    Article  Google Scholar 

  • Prevot, T., Homola, J. R., Martin, L. H., Mercer, J. S., & Cabrall, C. D. (2012). Toward automated air traffic control—Investigating a fundamental paradigm shift in human/systems interaction. International Journal of Human-Computer Interaction, 28(2), 77–98.

    Article  Google Scholar 

  • Ronchi, A. M., & Ronchi, A. M. (2019). e-Government: Background, today’s implementation and future trends. e-Democracy: Toward a New Model of (Inter) active Society, 93–196.

  • Ros, F., Guillaume, S., Riad, R., & El Hajji, M. (2022). Detection of natural clusters via S-DBSCAN a self-tuning version of DBSCAN. Knowledge-Based Systems, 241, 108288.

    Article  Google Scholar 

  • Roslan, F. A. B. M., & Ahmad, N. B. (2023). The rise of AI-powered voice assistants: Analyzing their transformative impact on modern customer service paradigms and consumer expectations. Quarterly Journal of Emerging Technologies and Innovations, 8(3), 33–64.

    Google Scholar 

  • Rutaganda, L., Bergstrom, R., Jayashekhar, A., Jayasinghe, D., & Ahmed, J. (2017). Avoiding pitfalls and unlocking real business value with RPA. Journal of Financial Transformation, 46(11), 104–115.

    Google Scholar 

  • Ryll, L., Barton, M. E., Zhang, B. Z., McWaters, R. J., Schizas, E., Hao, R., ... & Yerolemou, N. (2020). Transforming paradigms: A global AI in financial services survey. https://doi.org/10.2139/ssrn.3532038.

  • Samtani, S., Kantarcioglu, M., & Chen, H. (2020). Trailblazing the artificial intelligence for cybersecurity discipline: A multi-disciplinary research roadmap. ACM Transactions on Management Information Systems (TMIS), 11(4), 1–19.

    Article  Google Scholar 

  • Shaikh, T. A., Rasool, T., & Lone, F. R. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119.

    Article  Google Scholar 

  • Singam, A. (2023). Revolutionizing patient Care: A comprehensive review of artificial intelligence applications in anesthesia. Cureus, 15(12).

  • Stecuła, K., Wolniak, R., & Grebski, W. W. (2023). AI-driven urban energy solutions—From individuals to society: A review. Energies, 16(24), 7988.

    Article  Google Scholar 

  • Strusani, D., & Houngbonon, G. V. (2019). The role of artificial intelligence in supporting development in emerging markets. IFC World Bank Group

    Book  Google Scholar 

  • Taherdoost, H. (2023). An overview of trends in information systems: Emerging technologies that transform the information technology industry. Cloud Computing and Data Science, 1–16.

  • Tang, Y., & Lan, Y. (2021, April). Design of university financial decision-making platform based on data mining. In Journal of Physics: Conference Series (Vol. 1881, No. 4, p. 042063). IOP Publishing.

  • Tekic, Z., & Koroteev, D. (2019). From disruptively digital to proudly analog: A holistic typology of digital transformation strategies. Business Horizons, 62(6), 683–693.

    Article  Google Scholar 

  • Theodorou, T. I., Zamichos, A., Skoumperdis, M., Kougioumtzidou, A., Tsolaki, K., Papadopoulos, D., & Tzovaras, D. (2021). An AI-enabled stock prediction platform combining news and social sensing with financial statements. Future Internet, 13(6), 138.

    Article  Google Scholar 

  • Tiron-Tudor, A., & Deliu, D. (2022). Reflections on the human-algorithm complex duality perspectives in the auditing process. Qualitative Research in Accounting & Management, 19(3), 255–285.

    Google Scholar 

  • Tu, Z (2022). The new civilization upon data. Springer Books.

    Book  Google Scholar 

  • Varfa, R., Khare, U., Gupta, A., & Sharma, G. (2023). Robotic process automation: Chatbot integration for task automation (Banking Industry). Grenze International Journal of Engineering & Technology (GIJET), 9(2), 648–655.

    Google Scholar 

  • Villar, A. S., & Khan, N. (2021). Robotic process automation in banking industry: A case study on Deutsche Bank. Journal of Banking and Financial Technology, 5(1), 71–86.

    Google Scholar 

  • Villegas-Ch, W., Arias-Navarrete, A., & Palacios-Pacheco, X. (2020). Proposal of an architecture for the integration of a chatbot with artificial intelligence in a smart campus for the improvement of learning. Sustainability, 12(4), 1500.

    Article  Google Scholar 

  • Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924.

    Article  Google Scholar 

  • Wang, L., & Wang, Y. (2022). Supply chain financial service management system based on block chain IoT data sharing and edge computing. Alexandria Engineering Journal, 61(1), 147–158.

    Article  Google Scholar 

  • Wen, C., Dematties, D., & Zhang, S. L. (2021). A guide to signal processing algorithms for nanopore sensors. ACS Sensors, 6(10), 3536–3555.

    Article  Google Scholar 

  • West, D. M., & Allen, J. R. (2020). Turning point: Policymaking in the era of artificial intelligence. Brookings Institution Press.

    Google Scholar 

  • Xie, H., Zhang, L., Lim, C. P., Yu, Y., Liu, C., Liu, H., & Walters, J. (2019). Improving K-means clustering with enhanced firefly algorithms. Applied Soft Computing, 84, 105763.

    Article  Google Scholar 

  • Yu, P., Tao, Y., Zhang, J., & Jin, Y. (2022, August). Design and implementation of a cloud-native platform for financial big data processing course. In International Conference on Computer Science and Education (pp. 180–193). Singapore: Springer Nature Singapore.

    Google Scholar 

  • Yu, S., & Lu, Y. (2021). An introduction to artificial intelligence in education. Springer.

    Book  Google Scholar 

  • Zhang, C. (2020). Governing (through) trustworthiness: Technologies of power and subjectification in China’s social credit system. Critical Asian Studies, 52(4), 565–588.

    Article  Google Scholar 

  • Zhang, Y., Xiong, F., Xie, Y., Fan, X., & Gu, H. (2020). The impact of artificial intelligence and blockchain on the accounting profession. Ieee Access, 8, 110461–110477.

    Article  Google Scholar 

  • Zhang, D., Pee, L. G., & Cui, L. (2021). Artificial intelligence in E-commerce fulfillment: A case study of resource orchestration at Alibaba’s Smart Warehouse. International Journal of Information Management, 57, 102304.

    Article  Google Scholar 

  • Zhao, J., Rahbee, A., & Wilson, N. H. (2007). Estimating a rail passenger trip origin-destination matrix using automatic data collection systems. Computer-Aided Civil and Infrastructure Engineering, 22(5), 376–387.

    Article  Google Scholar 

  • Zheng, X. L., Zhu, M. Y., Li, Q. B., Chen, C. C., & Tan, Y. C. (2019). FinBrain: when finance meets AI 2.0. Frontiers of Information Technology & Electronic Engineering, 20(7), 914–924.

    Article  Google Scholar 

  • Zhou, Y., Wu, N., Hu, B., Zhang, Y., Qiu, J., & Cai, W. (2022). Implementation and performance of face recognition payment system securely encrypted by SM4 algorithm. Information, 13(7), 316.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Higher Education, Beijing Technology & Business University, Beijing, 100048, China

    Xiao Han

  2. Yangzhou Polytechnic College, Yangzhou, 225009, China

    Shumei Xiao, Jun Sheng & Guangtao Zhang

Authors
  1. Xiao Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shumei Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guangtao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shumei Xiao.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, X., Xiao, S., Sheng, J. et al. Enhancing Efficiency and Decision-Making in Higher Education Through Intelligent Commercial Integration: Leveraging Artificial Intelligence. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01868-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13132-024-01868-2

Keywords

Search

Navigation