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The Intersection of Quantum Computing, Artificial Intelligence and Financial Risks: A Bibliometric Analysis of the Modern Financial Sector | ||
| Journal of Information Technology Management | ||
| دوره 17، Special Issue on Strategic, Organizational, and Social Issues of Digital Transformation in Organizations، 2025، صفحه 3-23 اصل مقاله (1.68 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2025.100694 | ||
| نویسندگان | ||
| Adarsh Garg* 1؛ Monika Singh2؛ Manjeet Kumar3 | ||
| 1Professor, Head of Data Analytics Area, GL Bajaj Institute of Management and Research, Greater Noida, India. | ||
| 2Assistant Professor, Department of Management, BIT Mesra (Patna Campus), Patna, Bihar. | ||
| 3Assistant Professor, G L Bajaj Institute of Technology and Management, Greater Noida, India. | ||
| چکیده | ||
| The finance sector is experiencing substantial technological disruption as Quantum Computing and Artificial Intelligence (AI) continue to advance at a rapid pace. This study employs bibliometric analysis, specifically VOS Viewer, to investigate the academic environment at the intersection of financial risk, AI, and quantum computation. From 2014 to 2023, a comprehensive bibliometric analysis was performed on a total of 145 journal articles that were published in Scopus and Web of Sciences (WoS). Articles are categorized based on their homogeneity in the disciplines of Quantum Computing, Financial Risk, and AI, as well as their interdisciplinary compositions. The results, which include authorship trends, keyword dynamics, and linked works, are analyzed and presented. This extensive bibliometric analysis offers critical insights into contemporary research and pinpointing areas necessitating further exploration. As quantum computers and AI algorithms become more sophisticated, this paper investigates the potential weaknesses and issues that financial institutions may encounter. By analyzing the intersection of two transformative technologies, the report offers critical insights into the discourse surrounding the safeguarding of financial systems in the quantum era. The analysis not only enhances the quality of the review but also directs researchers to significant papers and identifies regions of publications, thereby facilitating a more comprehensive understanding of the research environment. | ||
| کلیدواژهها | ||
| Quantum Computing؛ Financial Risk؛ Artificial Intelligence؛ Bibliometric Analysis | ||
| مراجع | ||
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Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164-184.
Ali, S., Yue, T., & Abreu, R. (2022). When software engineering meets quantum computing. Communications of the ACM, 65(4), 84-88.
Amato, A., Guzzo, T., Loia, V., Moscato, V., & Picariello, A. (2023). Deep learning models for financial distress prediction: A survey. Neural Computing and Applications. Advanced online publication.
Anton, N., Doroftei, B., Curteanu, S., Catãlin, L., Ilie, O. D., Târcoveanu, F., & Bogdănici, C. M. (2022). A comprehensive review on the use of artificial intelligence in ophthalmology and future research directions. Diagnostics, 13(1), 100.
Bouland, A., van Dam, W., Joorati, H., Kerenidis, I., & Prakash, A. (2020). Prospects and challenges of quantum finance. arXiv preprint arXiv:2011.06492.
Chang, Y. J., Sie, M. F., Liao, S. W., & Chang, C. R. (2023). The prospects of quantum computing for quantitative finance and beyond. IEEE Nanotechnology Magazine.
Chen, C., Hu, Z., Liu, S., & Tseng, H. (2012). Emerging trends in regenerative medicine: A scientometric analysis in CiteSpace. Expert Opinion on Biological Therapy, 12(5), 593–608.
Chen, C. M., Tso, G. K. F., & He, K. (2024). Quantum Optimized Cost Based Feature Selection and Credit Scoring for Mobile Micro-financing. Computational Economics, 63(2), 919-950.
Eichler, H. G., Trusheim, M., Schwarzer‐Daum, B., Larholt, K., Zeitlinger, M., Brunninger, M., ... & Hirsch, G. (2022). Precision Reimbursement for Precision Medicine: Using Real‐World Evidence to Evolve From Trial‐and‐Project to Track‐and‐Pay to Learn‐and‐Predict. Clinical Pharmacology & Therapeutics, 111(1), 52-62.
ensen, R. I. T., & Iosifidis, A. (2023). Fighting Money Laundering With Statistics and Machine Learning.
Gao, B. (2022). The use of machine learning combined with data mining technology in financial risk prevention. Computational economics, 59(4), 1385-1405.
Gao, Y., Wang, Q., Xu, C., & Wang, J. (2023). Financial distress prediction based on an ensemble of deep learning and incremental learning. Symmetry, 14(9), 716.
Gómez, A., Leitao, Á., Manzano, A., Musso, D., Nogueiras, M. R., Ordóñez, G., & Vázquez, C. (2022). A survey on quantum computational finance for derivatives pricing and VaR. Archives of computational methods in engineering, 29(6), 4137-4163.
Gupta, S., Modgil, S., Bhatt, P. C., Jabbour, C. J. C., & Kamble, S. (2023). Quantum computing led innovation for achieving a more sustainable COVID-19 healthcare industry. Technovation, 120, 102544.
Hall, P., Cox, B., Dickerson, S., Ravi Kannan, A., Kulkarni, R., & Schmidt, N. (2021). A United States fair lending perspective on machine learning. Frontiers in Artificial Intelligence, 4, 695301.
Hicks, D., Wouters, P., Waltman, L., De Rijcke, S., & Rafols, I. (2015). Bibliometrics: the Leiden Manifesto for research metrics. Nature, 520(7548), 429-431.
Ibrahim, A., Thiruvady, D., Schneider, J. G., & Abdelrazek, M. (2020). The challenges of leveraging threat intelligence to stop data breaches. Frontiers in Computer Science, 2, 36.
Innan, N., Khan, M. A. Z., & Bennai, M. (2024). Quantum computing for electronic structure analysis: Ground state energy and molecular properties calculations. Materials Today Communications, 38, 107760.
Innan, N., Sawaika, A., Dhor, A., Dutta, S., Thota, S., Gokal, H., ... & Bennai, M. (2024). Financial fraud detection using quantum graph neural networks. Quantum Machine Intelligence, 6(1), 1-18.
Kar, Arpan Kumar, et al. "How could quantum computing shape information systems research–An editorial perspective and future research directions." International Journal of Information Management (2024): 102776.
Kute, D. V., Pradhan, B., Shukla, N., & Alamri, A. (2021). Deep learning and explainable artificial intelligence techniques applied for detecting money laundering–a critical review. IEEE Access, 9, 82300-82317.
Li, J., Li, Z., Ma, Y., Dong, Y., & Chen, C. (2023). Deep Learning with Latent Features for Financial Distress Prediction. IEEE Access. Advanced online publication.
Li, W., Paraschiv, F., & Sermpinis, G. (2022). A data-driven explainable case-based reasoning approach for financial risk detection. Quantitative Finance, 22(12), 2257-2274.
Lin, K., & Gao, Y. (2022). Model interpretability of financial fraud detection by group SHAP. Expert Systems with Applications, 210, 118354.
Medeiros Assef, F., & Arns Steiner, M. T. (2020). Ten-year evolution on credit risk research: a Systematic Literature Review approach and discussion. Ingeniería e investigación, 40(2), 50-71.
Moradi, S., Mohammadi, S. D., Aghajani Bazzazi, A., Aali Anvari, A., & Osmanpour, A. (2022). Financial Risk Management Prediction of Mining and Industrial Projects using a Combination of Artificial Intelligence and Simulation Methods. Journal of Mining and Environment, 13(4), 1211-1223.
Pandl, K. D., Thiebes, S., Schmidt-Kraepelin, M., & Sunyaev, A. (2020). On the convergence of artificial intelligence and distributed ledger technology: A scoping review and future research agenda. IEEE Access, 8, 57075-57095.
Pol, S., & Ambekar, S. S. (2022). Predicting credit ratings using deep learning models–an analysis of the Indian it industry. Australasian Accounting, Business and Finance Journal, 16(5), 38-51.
Radanliev, P. (2024). Artificial intelligence and quantum cryptography. Journal of Analytical Science and Technology, 15(1), 4.
Saini, R., Bera, A., Behera, B. K., Ahmed, E. A., Jamjoom, M., & Farouk, A. (2023). Designing quantum blockchain system integrated with 6G network. Journal of King Saud University-Computer and Information Sciences, 35(10), 101847.
Sun, Q., Wu, H., & Zhao, B. (2022). Artificial intelligence technology in internet financial edge computing and analysis of security risk. International Journal of Ad Hoc and Ubiquitous Computing, 39(4), 201-210.
Takeda, A., Fujiwara, S., & Kanamori, T. (2014). Extended robust support vector machine based on financial risk minimization. Neural Computation, 26(11), 2541-2569.
Vaiyapuri,T., K. Priyadarshini, A. Hemlathadhevi, M. Dhamodaran, Dutta, A., Pustokhina, I.V., Pustokhin, D., 2022., Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model, Computers, Materials \& Continua, 72 (2), 2429—2444, retrieved from http://www.techscience.com/cmc/v72n2/47226
Wang, B. (2022). A financial risk identification model based on artificial intelligence. Wireless Networks, 1-9.
Wang, L., & Lee, R. S. (2022, November). The Design and Implementation of Quantum Finance Software Development Kit (QFSDK) for AI Education. In 2022 20th International Conference on Information Technology Based Higher Education and Training (ITHET) (pp. 1-7). IEEE.
Wei, L., Liu, H., Xu, J., Shi, L., Shan, Z., Zhao, B., & Gao, Y. (2023). Quantum machine learning in medical image analysis: A survey. Neurocomputing, 525, 42-53.
Yang, M., Lim, M. K., Qu, Y., Ni, D., & Xiao, Z. (2023). Supply chain risk management with machine learning technology: A literature review and future research directions. Computers & Industrial Engineering, 175, 108859.
Yu, L., Härdle, W. K., Borke, L., & Benschop, T. (2023). An AI approach to measuring financial risk. The Singapore Economic Review, 68(05), 1529-1549.
Zhang, L., Alsubai, S., Alqahtani, A., Alanazi, A., & Abualigah, L. Leveraging quantum‐inspired chimp optimization and deep neural networks for enhanced profit forecasting in financial accounting systems. Expert Systems, e13563.
Zhang, R., Wang, J., Jiang, N., & Wang, Z. (2023). Quantum support vector machine without iteration. Information Sciences, 635, 25-41.
Zhang, T., Zhu, W., Guo, Z., Li, S., & Li, Z. (2023). Incremental Support Vector Machine Ensemble Method for Financial Distress Prediction. Journal of Electrical and Computer Engineering, 2023, 8865823.
Zhang, Y., Luo, M., Wu, P., Wu, S., Lee, T. Y., & Bai, C. (2022). Application of computational biology and artificial intelligence in drug design. International journal of molecular sciences, 23(21), 13568.
Zhang, Y., Wu, Q., Li, H., Guo, J., Zhang, Y., & Xu, Y. (2023). Incremental Gradient Boosting Decision Trees Algorithm with Class Balancing for Financial Distress Prediction. IEEE Access. Advanced online publication.
Zhu, W., Zhang, T., Wu, Y., Li, S., & Li, Z. (2022). Research on optimization of an enterprise financial risk early warning method based on the DS-RF model. International review of financial analysis, 81, 102140.
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