The Transformative Power of Generative AI in Financial Service: A Comprehensive Review

Dorin Bayraktar; Eduard Alexandru Stoica; Ioana Andreea Bogoslov; Radu Mircea Georgescu

doi:10.18778/2082-4440.47.03

Autor

Dorin Bayraktar Al. I. Cuza University of Iasi, Faculty of Economics and Business Administration, Iasi, Romania https://orcid.org/0000-0002-2130-1764
Eduard Alexandru Stoica Lucian Blaga University of Sibiu, Faculty of Economic Sciences, Sibiu, Romania https://orcid.org/0000-0002-0693-8433
Ioana Andreea Bogoslov Lucian Blaga University of Sibiu, Faculty of Economic Sciences, Sibiu, Romania https://orcid.org/0000-0001-5834-8710
Radu Mircea Georgescu Al. I. Cuza University of Iasi, Faculty of Economics and Business Administration, Iasi, Romania https://orcid.org/0000-0002-7022-3715

DOI:

https://doi.org/10.18778/2082-4440.47.03

Słowa kluczowe:

GenAI, usługi finansowe, automatyzacja, efektywność operacyjna, etyka AI, prywatność danych

Abstrakt

W obecnych czasach, w których technologie cyfrowe są wszechobecne w większości ludzkich działań, transformacja cyfrowa pozostaje kluczowym obszarem badań o globalnym zasięgu. Wśród tych technologii, generatywna sztuczna inteligencja (GenAI) staje się szczególnie przełomową potęgą. Przekształca ona branże poprzez automatyzację procesów, usprawnianie procesu decyzyjnego i napędzanie innowacji biznesowych.

Głównym celem niniejszego artykułu jest przegląd i synteza literatury w celu zdefiniowania sztucznej inteligencji generatywnej i jej wpływu na branżę usług finansowych, przy jednoczesnym przedstawieniu pozytywnych i negatywnych aspektów wykorzystania tej technologii.

Artykuł składa się z dwóch głównych części. Pierwsza część ma na celu zdefiniowanie technologii GenAI, analizę jej istoty i sposobu działania, a druga część obejmuje przegląd literatury na temat wpływu sztucznej inteligencji generatywnej na usługi finansowe, wskazując w ten sposób zarówno zalety, jak i negatywne aspekty korzystania z tej technologii.

Wyniki badań mogą być cenne dla osób, które nie są zaznajomione z technologią GenAI lub są zainteresowane jej wpływem na środowisko biznesowe, w szczególności usługi finansowe.

Pobrania

Statystyki pobrań niedostępne.

Bibliografia

Adavala, Kiran Mayee. (2024), Deep Generative Models for Data Synthesis and Augmentation in Machine Learning. JES 20, 1242–1249. https://doi.org/10.52783/jes.1435 DOI: https://doi.org/10.52783/jes.1435

Addy, W.A., Ajayi-Nifise, A.O., Bello, B.G., Tula, S.T., Odeyemi, O., Falaiye, T. (2024), Transforming financial planning with AI-driven analysis: A review and application insights. World Journal of Advanced Engineering Technology and Sciences 11, 240–257. https://doi.org/10.30574/wjaets.2024.11.1.0053 DOI: https://doi.org/10.30574/wjaets.2024.11.1.0053

Aldausari, N., Sowmya, A., Marcus, N., Mohammadi, G. (2020), Video Generative Adversarial Networks: A Review.

Alwahedi, F., Aldhaheri, A., Ferrag, M.A., Battah, A., Tihanyi, N. (2024), Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models. Internet of Things and Cyber-Physical Systems 4, 167–185. https://doi.org/10.1016/j.iotcps.2023.12.003 DOI: https://doi.org/10.1016/j.iotcps.2023.12.003

Amutha, A. (2023), Customer Segmentation using Machine Learning Techniques. Tuijin Jishu/Journal of Propulsion Technology 44, 2051–2061. https://doi.org/10.52783/tjjpt.v44.i3.653 DOI: https://doi.org/10.52783/tjjpt.v44.i3.653

Arpaci, I. (2023), A Multi-Analytical SEM-ANN Approach to Investigate the Social Sustainability of AI Chatbots Based on Cybersecurity and Protection Motivation Theory | Request PDF. https://www.researchgate.net/publication/376251815_A_Multi-Analytical_SEM-ANN_Approach_to_Investigate_the_Social_Sustainability_of_AI_Chatbots_Based_on_Cybersecurity_and_Protection_Motivation_Theory (accessed 8.16.24).

Bandi, A., Adapa, P., Kuchi, Y. (2023), The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges. Future Internet 15, 260. https://doi.org/10.3390/fi15080260 DOI: https://doi.org/10.3390/fi15080260

Banovic, N., Yang, Z., Ramesh, A., Liu, A. (2023), Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust. Proceedings of the ACM on Human-Computer Interaction 7, 1–17. https://doi.org/10.1145/3579460 DOI: https://doi.org/10.1145/3579460

Bermano, A., Gal, R., Alaluf, Y., Mokady, R., Nitzan, Y., Tov, O., Patashnik, O., Cohen-Or, D. (2022), State-of-the-Art in the Architecture, Methods and Applications of StyleGAN. DOI: https://doi.org/10.1111/cgf.14503

Bilgram, V., Laarmann, F. (2023), Accelerating Innovation With Generative AI: AI-Augmented Digital Prototyping and Innovation Methods. IEEE Engineering Management Review PP, 1–5. https://doi.org/10.1109/EMR.2023.3272799 DOI: https://doi.org/10.1109/EMR.2023.3272799

Bodendorf, F., Franke, J. (2024), The Technological Transformation Process for Dynamic Capabilities in Business Operations. IEEE Transactions on Engineering Management 71, 3671–3687. https://doi.org/10.1109/TEM.2024.3349478 DOI: https://doi.org/10.1109/TEM.2024.3349478

Bonelli, M.I., Döngül, E. (2023), Robo-Advisors in the Financial Services Industry: Recommendations for Full-Scale Optimization, Digital Twin Integration, and Leveraging Natural Language Processing Trends. https://www.research-gate.net/publication/372214336_Robo-Advisors_in_the_Financial_Services_Industry_Recommendations_for_Full-Scale_Optimization_Digital_Twin_Integration_and_Leveraging_Natural_Language_Processing_Trends (accessed 7.24.24). DOI: https://doi.org/10.1109/ICVR57957.2023.10169615

Brynjolfsson, E., Li, D., Raymond, L. (2023), Generative AI at Work. https://doi.org/10.48550/arXiv.2304.11771 DOI: https://doi.org/10.3386/w31161

Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P.S., Sun, L. (2023), A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT. https://doi.org/10.48550/arXiv.2303.04226

Chakraborty, T., S., U.R.K., Naik, S.M., Panja, M., Manvitha, B. (2024), Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art. Mach. Learn.: Sci. Technol. 5, 011001. https://doi.org/10.1088/2632-2153/ad1f77 DOI: https://doi.org/10.1088/2632-2153/ad1f77

Chen, B., Wu, Z., Zhao, R. (2023), From Fiction to Fact: The Growing Role of Generative AI in Business and Finance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4528225 DOI: https://doi.org/10.2139/ssrn.4528225

Chi, N.T.K., Hoang Vu, N. (2023), Investigating the customer trust in artificial intelligence: The role of anthropomorphism, empathy response, and interaction. CAAI Transactions on Intelligence Technology 8, 260–273. https://doi.org/10.1049/cit2.12133 DOI: https://doi.org/10.1049/cit2.12133

Chitty-Venkata, K.T., Emani, M., Vishwanath, V., Somani, A. (2022), Neural Architecture Search for Transformers: A Survey. IEEE Access PP, 1–1. https://doi.org/10.1109/ACCESS.2022.3212767 DOI: https://doi.org/10.1109/ACCESS.2022.3212767

Cronin, I. (2024), Understanding Generative AI Business Applications: A Guide to Technical Principles and Real-World Applications. Apress, Berkeley, CA. https://doi.org/10.1007/979-8-8688-0282-9 DOI: https://doi.org/10.1007/979-8-8688-0282-9

Cui, Y., Song, Y., Sun, C., Howard, A., Belongie, S. (2018), Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning, 4109–4118. https://doi.org/10.1109/CVPR.2018.00432 DOI: https://doi.org/10.1109/CVPR.2018.00432

Deshpande, A. (2024), Regulatory Compliance and AI: Navigating the Legal and Regulatory Challenges of AI in Finance https://ieeexplore.ieee.org/abstract/document/10616752 (accessed 8.13.24).

Dihingia, H., Ahmed, S., Borah, D., Gupta, S., Phukan, K., Muchahari, M.K. (2021), Chatbot Implementation in Customer Service Industry through Deep Neural Networks, in: 2021 International Conference on Computational Performance Evaluation (ComPE). Presented at the 2021 International Conference on Computational Performance Evaluation (ComPE), 193–198. https://doi.org/10.1109/ComPE53109.2021.9752271 DOI: https://doi.org/10.1109/ComPE53109.2021.9752271

Divya, V., Mirza, A.U. (2024), Exploring the Frontiers of Artificial Intelligence and Machine Learning Technologies CHAPTER 8 Transforming Content Creation: The Influence of Generative AI on a New Frontier, p. 17.

Ebert, C., Louridas, P. (2023), Generative AI for Software Practitioners. IEEE Software 40, 30–38. https://doi.org/10.1109/MS.2023.3265877 DOI: https://doi.org/10.1109/MS.2023.3265877

Epstein, Z., Hertzmann, A., Herman, L., Mahari, R., Frank, M., Groh, M., Schroeder, H., Smith, A., Akten, M., Fjeld, J., Farid, H., Leach, N., Pentland, A., Russakovsky, O. (2023), Art and the science of generative AI: A deeper dive. DOI: https://doi.org/10.1126/science.adh4451

Feuerriegel, S., Hartmann, J., Janiesch, C., Zschech, P. (2024), Generative AI. Bus Inf Syst Eng 66, 111–126. https://doi.org/10.1007/s12599-023-00834-7 DOI: https://doi.org/10.1007/s12599-023-00834-7

Gm, H., Gourisaria, M., Pandey, M., Rautaray, S. (2020), A comprehensive survey and analysis of generative models in machine learning. Computer Science Review 38, 100285. https://doi.org/10.1016/j.cosrev.2020.100285 DOI: https://doi.org/10.1016/j.cosrev.2020.100285

Grange, C., Demazure, T., Ringeval, M., Bourdeau, S., Martineau, C. (2024), The Human-GenAI Value Loop in Human-Centered Innovation: Beyond the Magical Narrative. https://doi.org/10.48550/arXiv.2407.17495 DOI: https://doi.org/10.1111/isj.12602

Gupta, M., Akiri, C., Aryal, K., Parker, E., Praharaj, L. (2023), From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy. IEEE Access PP, 1–1. https://doi.org/10.1109/ACCESS.2023.3300381 DOI: https://doi.org/10.1109/ACCESS.2023.3300381

Gupta, P., Ding, B., Guan, C., Ding, D. (2024), Generative AI: A systematic review using topic modelling techniques. Data and Information Management, Systematic Review and Meta-analysis in Information Management Research 8, 100066. https://doi.org/10.1016/j.dim.2024.100066 DOI: https://doi.org/10.1016/j.dim.2024.100066

Hentzen, J.K., Hoffmann, A., Dolan, R., Pala, E. (2022), Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research. IJBM 40, 1299–1336. https://doi.org/10.1108/IJBM-09-2021-0417 DOI: https://doi.org/10.1108/IJBM-09-2021-0417

Hofmann, P., Rückel, T., Urbach, N. (2021), Innovating with Artificial Intelligence: Capturing the Constructive Functional Capabilities of Deep Generative Learning. https://doi.org/10.24251/HICSS.2021.669 DOI: https://doi.org/10.24251/HICSS.2021.669

How, M.-L., Cheah, S.-M., Khor, A., Chan, Y. (2020), Artificial Intelligence-Enhanced Predictive Insights for Advancing Financial Inclusion: A Human-Centric AI-Thinking Approach. Big Data and Cognitive Computing 4, 8. https://doi.org/10.3390/bdcc4020008 DOI: https://doi.org/10.3390/bdcc4020008

Huang, B., Huan, Y., Li Da Xu, Zheng, L., ZouTo, Z. (2018), Automated trading systems statistical and machine learning methods and hardware implementation: a survey. https://www.researchgate.net/publication/326361736_Automated_trading_systems_statistical_and_machine_learning_methods_and_hardware_implementation_a_survey (accessed 8.11.24).

Huang, K., Goertzel, B., Wu, D., Xie, A. (2024), GenAI Model Security, in: Huang, K., Wang, Y., Goertzel, B., Li, Y., Wright, S., Ponnapalli, J. (Eds.), Generative AI Security: Theories and Practices. Springer Nature Switzerland, Cham, 163–198. https://doi.org/10.1007/978-3-031-54252-7_6 DOI: https://doi.org/10.1007/978-3-031-54252-7_6

Ijiga, O.M., Idoko, P.I., Anebi Enyejo, L., Akoh, O., Ileanaju Ugbane, S., Ime Ibokette, A. (2024), Harmonizing the voices of AI: Exploring generative music models, voice cloning, and voice transfer for creative expression. World J. Adv. Eng. Technol. Sci. 11, 372–394. https://doi.org/10.30574/wjaets.2024.11.1.0072 DOI: https://doi.org/10.30574/wjaets.2024.11.1.0072

Jain, L., Menon, V. (2023), AI Algorithmic Bias: Understanding its Causes, Ethical and Social Implications, 460–467. https://doi.org/10.1109/ICTAI59109.2023.00073 DOI: https://doi.org/10.1109/ICTAI59109.2023.00073

Jain, R., Thareja, U. (2019), Artificial intelligence enabled in-video advertising: Infiltrating the fashion industry.

Kalota, F. (2024), A Primer on Generative Artificial Intelligence. Education Sciences 14, 172. https://doi.org/10.3390/educsci14020172 DOI: https://doi.org/10.3390/educsci14020172

Kamath, P., Morreale, F., Bagaskara, P.L., Wei, Y., Nanayakkara, S. (2024), Sound Designer-Generative AI Interactions: Towards Designing Creative Support Tools for Professional Sound Designers, in: Proceedings of the CHI Conference on Human Factors in Computing Systems. Presented at the CHI ’24: CHI Conference on Human Factors in Computing Systems, ACM, Honolulu HI USA, 1–17. https://doi.org/10.1145/3613904.3642040 DOI: https://doi.org/10.1145/3613904.3642040

Karthik V, K. (2023), Applications of Machine Learning in Predictive Analysis and Risk Management in Trading. https://www.researchgate.net/publication/376283081_Applications_of_Machine_Learning_in_Predictive_Analysis_and_Risk_Management_in_Trading (accessed 7.8.24).

Khuntia, J., Saldanha, T., Kathuria, A., Tanniru, M.R. (2024), Digital service flexibility: a conceptual framework and roadmap for digital business transformation. European Journal of Information Systems 33, 61–79. https://doi.org/10.1080/0960085X.2022.2115410 DOI: https://doi.org/10.1080/0960085X.2022.2115410

Kim, S., Woo, J. (2022), Explainable AI framework for the financial rating models: Explaining framework that focuses on the feature influences on the changing classes or rating in various customer models used by the financial institutions, 252–255. https://doi.org/10.1145/3497623.3497664 DOI: https://doi.org/10.1145/3497623.3497664

Koga, S. (2023), The Integration of Large Language Models Such as ChatGPT in Scientific Writing: Harnessing Potential and Addressing Pitfalls. Korean Journal of Radiology 24. https://doi.org/10.3348/kjr.2023.0738 DOI: https://doi.org/10.3348/kjr.2023.0738

Koleva, G., Krcmar, H. (2018), Reducing false positives in fraud detection: Combining the red flag approach with process mining. International Journal of Accounting Information Systems 31. https://doi.org/10.1016/j.accinf.2018.03.004 DOI: https://doi.org/10.1016/j.accinf.2018.03.004

Koshiyama, A., Firoozye, N., Treleaven, P. (2020), Generative adversarial networks for financial trading strategies fine-tuning and combination. Quantitative Finance 21, 1–17. https://doi.org/10.1080/14697688.2020.1790635 DOI: https://doi.org/10.1080/14697688.2020.1790635

Leso, B.H., Cortimiglia, M.N., Ghezzi, A., Minatogawa, V. (2024), Exploring digital transformation capability via a blended perspective of dynamic capabilities and digital maturity: a pattern matching approach. Rev Manag Sci 18, 1149–1187. https://doi.org/10.1007/s11846-023-00692-3 DOI: https://doi.org/10.1007/s11846-023-00692-3

Lopez-Jimenez, F., Attia, Z., Arruda-Olson, A., Carter, R., Chareonthaitawee, P., Jouni, H., Kapa, S., Lerman, A., Luong, C., Medina-Inojosa, J., Noseworthy, P., Pellikka, P., Redfield, M., Roger, V., Sandhu, G., Senecal, C., Friedman, P. (2020), Artificial Intelligence in Cardiology: Present and Future. Mayo Clinic Proceedings 95, 1015–1039. https://doi.org/10.1016/j.mayocp.2020.01.038 DOI: https://doi.org/10.1016/j.mayocp.2020.01.038

Luo, X., Yang, Y., Yin, S., Li, H., Zhang, W.-J., Xu, G.-X., Fan, W., Zheng, D., Li, Jianpeng, Shen, D., Gao, Y., Shao, Y., Ban, X., Li, Jing, Lian, S.-S., Zhang, C., Ma, L., Lin, C., Luo, Y., Zhou, F., Wang, S., Sun, Y., Zhang, R., Xie, C. (2022), False-Negative and False-Positive Outcomes Of An Artificial Intelligence System And Observers on Brain Metastasis Detection: Secondary Analysis of a Prospective, Multicentre, Multireader Study. https://doi.org/10.2139/ssrn.4071504 DOI: https://doi.org/10.2139/ssrn.4071504

Manahov, V., Zhang, H. (2019), Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming. https://www.researchgate.net/publication/330186910_Forecasting_Financial_Markets_Using_High-Frequency_Trading_Data_Examination_with_Strongly_Typed_Genetic_Programming (accessed 2.20.23). DOI: https://doi.org/10.1080/10864415.2018.1512271

Mishra, S. (2023), Exploring the Impact of AI-Based Cyber Security Financial Sector Management. Applied Sciences 13, 5875. https://doi.org/10.3390/app13105875 DOI: https://doi.org/10.3390/app13105875

Montemayor, C., Halpern, J., Fairweather, A. (2022), In principle obstacles for empathic AI: why we can’t replace human empathy in healthcare. AI & Soc 37, 1353–1359. https://doi.org/10.1007/s00146-021-01230-z DOI: https://doi.org/10.1007/s00146-021-01230-z

Mungoli, N. (2023), Revolutionizing Industries: The Impact of Artificial Intelligence Technologies. https://doi.org/10.11648/j.ajai.20220601.01

Neupane, S., Fernandez, I.A., Mittal, S., Rahimi, S. (2023), Impacts and Risk of Generative AI Technology on Cyber Defense. https://doi.org/10.48550/arXiv.2306.13033

Ogunpola, A., Saeed, F., Basurra, S., Albarrak, A.M., Qasem, S.N. (2024), Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases. Diagnostics 14, 144. https://doi.org/10.3390/diagnostics14020144 DOI: https://doi.org/10.3390/diagnostics14020144

Qirui Ju. (2023), Experimental Evidence on Negative Impact of Generative AI on Scientific Learning Outcomes. https://www.researchgate.net/publication/374010921_Experimental_Evidence_on_Negative_Impact_of_Generative_AI_on_Scientific_Learning_Outcomes (accessed 6.14.24).

Raju, P.V.M., Sumallika, T. (2023), The Impact of AI in the Global Economy and its Implications in Industry 4.0 Era. Inf. Tech. Educ. Soc 18, 53–62. https://doi.org/10.7459/ites/18.2.05 DOI: https://doi.org/10.7459/ites/18.2.05

Rakha, N.A. (2023), The impacts of Artificial Intelligence (AI) on business and its regulatory challenges. International Journal of Law and Policy 1. https://doi.org/10.59022/ijlp.23 DOI: https://doi.org/10.59022/ijlp.23

Sachan, S., Yang J.-B., Xu, D.-L., Benavides, D.E. (2019), An Explainable AI Decision-Support-System to Automate Loan Underwriting. https://www.researchgate.net/publication/337563997_An_Explainable_AI_Decision-Support-System_to_Automate_Loan_Underwriting (accessed 7.13.23).

Sadok, H., Sakka, F., El Maknouzi, M. (2022), Artificial intelligence and bank credit analysis: A review. Cogent Economics & Finance 10. https://doi.org/10.1080/23322039.2021.2023262 DOI: https://doi.org/10.1080/23322039.2021.2023262

Sahare, P. (2023), InvestAI: Connecting With Future Gains. IJRASET 11, 2054–2057. https://doi.org/10.22214/ijraset.2023.57018 DOI: https://doi.org/10.22214/ijraset.2023.57018

Shelf. (2024), Neural Networks and How They Work With Generative AI. https://shelf.io/blog/neural-networks-and-how-they-work-with-generative-ai/ (accessed 5.10.24).

Shilpa N S, Ms. (2023), Chatbot for MindTech Digital Solutions. IJRASET 11, 1534–1537. https://doi.org/10.22214/ijraset.2023.57672 DOI: https://doi.org/10.22214/ijraset.2023.57672

Singh, A., Ahlawat, N. (2023), A Review Article: The Growing Role Of Data Science And Ai In Banking And Finance. Open Access 05.

Singh, D.N., Ahuja, D.S. (2024), Artificial Intelligence (AI) and Business. Kitab writing publication.

Strobelt, H., Kinley, J., Krueger, R., Beyer, J., Pfister, H., Rush, A. (2021). GenNI: Human-AI Collaboration for Data-Backed Text Generation. IEEE Transactions on Visualization and Computer Graphics PP, 1–1. https://doi.org/10.1109/TVCG.2021.3114845 DOI: https://doi.org/10.1109/TVCG.2021.3114845

Sun, J., Liao, V., Muller, M., Agarwal, M., Houde, S., Talamadupula, K., Weisz, J. (2022), Investigating Explainability of Generative AI for Code through Scenario-based Design, 212–228. https://doi.org/10.1145/3490099.3511119 DOI: https://doi.org/10.1145/3490099.3511119

Takyar, A. (2023), AI in loan underwriting: Use cases, technologies, solution and implementation. LeewayHertz – AI Development Company. https://www.leewayhertz.com/ai-loan-underwriting/ (accessed 7.13.23).

Tiezzi, M., Casoni, M., Betti, A., Gori, M., Melacci, S. (2024), State-Space Modeling in Long Sequence Processing: A Survey on Recurrence in the Transformer Era. https://doi.org/10.48550/arXiv.2406.09062

Wang, M., Fu, W., He, X., Hao, S., Wu, X. (2020), A Survey on Large-scale Machine Learning. https://doi.org/10.48550/arXiv.2008.03911 DOI: https://doi.org/10.1109/TKDE.2020.3015777

Xu, Y., Shieh, C.-H., van Esch, P., Ling, I.-L. (2020), AI customer service: Task complexity, problem-solving ability, and usage intention. Australasian Marketing Journal (AMJ) 28, 189–199. https://doi.org/10.1016/j.ausmj.2020.03.005 DOI: https://doi.org/10.1016/j.ausmj.2020.03.005

Yuanming Ding, Wei Kang, Jianxin Feng, Bo Peng, Anna Yang (2023), Credit Card Fraud Detection Based on Improved Variational Autoencoder Generative Adversarial Network. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10210017 (accessed 5.21.24).

Zhai, C., Wibowo, S., Li, L.D. (2024), The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learning Environments 11, 28. https://doi.org/10.1186/s40561-024-00316-7 DOI: https://doi.org/10.1186/s40561-024-00316-7

Zhang, L., Wu, X., Wang, F., Sun, A., Cui, L., Liu, J. (2024), Edge-Based Video Stream Generation for Multi-Party Mobile Augmented Reality. IEEE Transactions on Mobile Computing 23, 409–422. https://doi.org/10.1109/TMC.2022.3232543 DOI: https://doi.org/10.1109/TMC.2022.3232543

Zhang, X., Yadollahi, M.M., Dadkhah, S., Isah, H., Le, D.-P., Ghorbani, A.A. (2022), Data breach: analysis, countermeasures and challenges. International Journal of Information and Computer Security 19, 402–442. https://doi.org/10.1504/IJICS.2022.127169 DOI: https://doi.org/10.1504/IJICS.2022.127169

Zhou, P., Wang, L., Liu, Z., Hao, Y., Hui, P., Tarkoma, S., Kangasharju, J. (2024), A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming. DOI: https://doi.org/10.36227/techrxiv.171172801.19993069/v1

Zohuri, B. (2023), Charting the Future The Synergy of Generative AI, Quantum Computing, and the Transformative Impact on Economy, Society, Jobs Market, and the Emergence of Artificial Super Intelligence. Current Trends in Eng Sc 3, 1–4. https://doi.org/10.54026/CTES/1050 DOI: https://doi.org/10.54026/CTES/1050