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

dc.contributor.authorBayraktar, Dorin
dc.contributor.authorStoica, Eduard Alexandru
dc.contributor.authorBogoslov, Ioana Andreea
dc.contributor.authorGeorgescu, Radu Mircea
dc.date.accessioned2025-12-22T09:05:23Z
dc.date.available2025-12-22T09:05:23Z
dc.date.issued2024-12-31
dc.identifier.issn2082-4440
dc.identifier.urihttp://hdl.handle.net/11089/57098
dc.description.abstractIn the current context, where digital technologies are ubiquitous in most human activities, digital transformation remains a key area of research with global effects. Among these technologies, generative AI (GenAI) is emerging as a particularly disruptive force. It is transforming industries by automating processes, improving decision-making and driving business innovation.The main objective of this paper is to review and synthesize the literature to define artificial generative intelligence and how it influences the financial services industry, presenting the positive and negative aspects of the use of this technology.The paper comprises two major parts. The first part aims to define the GenAI technology, analyzing its essence and how it works, and the second part comprises a literature review on how artificial generative intelligence influences financial services, thus highlighting both the advantages, as well as the negative aspects of using the technology.The research findings could be valuable to individuals who are unfamiliar with GenAI technology, or are interested in its impact on the business environment, in particular financial services.en
dc.description.abstractW 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.pl
dc.language.isoen
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl
dc.relation.ispartofseriesEkonomia Międzynarodowa;47pl
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectGenAIen
dc.subjectfinancial servicesen
dc.subjectautomationen
dc.subjectoperational efficiencyen
dc.subjectAI ethicsen
dc.subjectdata privacyen
dc.subjectGenAIpl
dc.subjectusługi finansowepl
dc.subjectautomatyzacjapl
dc.subjectefektywność operacyjnapl
dc.subjectetyka AIpl
dc.subjectprywatność danychpl
dc.titleThe Transformative Power of Generative AI in Financial Service: A Comprehensive Reviewen
dc.title.alternativeTransformacyjna siła generatywnej sztucznej inteligencji w usługach finansowych: kompleksowy przeglądpl
dc.typeArticle
dc.page.number44-75
dc.contributor.authorAffiliationBayraktar, Dorin - Al. I. Cuza University of Iasi, Faculty of Economics and Business Administration, Iasi, Romaniaen
dc.contributor.authorAffiliationStoica, Eduard Alexandru - Lucian Blaga University of Sibiu, Faculty of Economic Sciences, Sibiu, Romaniaen
dc.contributor.authorAffiliationBogoslov, Ioana Andreea - Lucian Blaga University of Sibiu, Faculty of Economic Sciences, Sibiu, Romaniaen
dc.contributor.authorAffiliationGeorgescu, Radu Mircea - Al. I. Cuza University of Iasi, Faculty of Economics and Business Administration, Iasi, Romaniaen
dc.identifier.eissn2300-6005
dc.referencesAdavala, Kiran Mayee. (2024), Deep Generative Models for Data Synthesis and Augmentation in Machine Learning. JES 20, 1242–1249. https://doi.org/10.52783/jes.1435en
dc.referencesAddy, 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.2024.11.1.0053en
dc.referencesAldausari, N., Sowmya, A., Marcus, N., Mohammadi, G. (2020), Video Generative Adversarial Networks: A Review.en
dc.referencesAlwahedi, 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.2023.12.003en
dc.referencesAmutha, A. (2023), Customer Segmentation using Machine Learning Techniques. Tuijin Jishu/Journal of Propulsion Technology 44, 2051–2061. https://doi.org/10.52783/tjjpt.v44.i3.653en
dc.referencesArpaci, 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).en
dc.referencesBandi, 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/fi15080260en
dc.referencesBanovic, 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/10.1145/3579460en
dc.referencesBermano, 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.en
dc.referencesBilgram, 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.2023.3272799en
dc.referencesBodendorf, 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.2024.3349478en
dc.referencesBonelli, 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).en
dc.referencesBrynjolfsson, E., Li, D., Raymond, L. (2023), Generative AI at Work. https://doi.org/10.48550/arXiv.2304.11771en
dc.referencesCao, 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.2303.04226en
dc.referencesChakraborty, 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/ad1f77en
dc.referencesChen, 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.4528225en
dc.referencesChi, 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/cit2.12133en
dc.referencesChitty-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.2022.3212767en
dc.referencesCronin, I. (2024), Understanding Generative AI Business Applications: A Guide to Technical Principles and Real-World Applications. Apress, Berkeley, CA. https://doi.org/10.1007/979-8-8688-0282-9en
dc.referencesCui, 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.2018.00432en
dc.referencesDeshpande, 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).en
dc.referencesDihingia, 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/ComPE53109.2021.9752271en
dc.referencesDivya, 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.en
dc.referencesEbert, C., Louridas, P. (2023), Generative AI for Software Practitioners. IEEE Software 40, 30–38. https://doi.org/10.1109/MS.2023.3265877en
dc.referencesEpstein, 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.en
dc.referencesFeuerriegel, S., Hartmann, J., Janiesch, C., Zschech, P. (2024), Generative AI. Bus Inf Syst Eng 66, 111–126. https://doi.org/10.1007/s12599-023-00834-7en
dc.referencesGm, 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.2020.100285en
dc.referencesGrange, 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.2407.17495en
dc.referencesGupta, 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.2023.3300381en
dc.referencesGupta, 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.2024.100066en
dc.referencesHentzen, 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-09-2021-0417en
dc.referencesHofmann, 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.2021.669en
dc.referencesHow, 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/bdcc4020008en
dc.referencesHuang, 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).en
dc.referencesHuang, 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_6en
dc.referencesIjiga, 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.2024.11.1.0072en
dc.referencesJain, L., Menon, V. (2023), AI Algorithmic Bias: Understanding its Causes, Ethical and Social Implications, 460–467. https://doi.org/10.1109/ICTAI59109.2023.00073en
dc.referencesJain, R., Thareja, U. (2019), Artificial intelligence enabled in-video advertising: Infiltrating the fashion industry.en
dc.referencesKalota, F. (2024), A Primer on Generative Artificial Intelligence. Education Sciences 14, 172. https://doi.org/10.3390/educsci14020172en
dc.referencesKamath, 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/10.1145/3613904.3642040en
dc.referencesKarthik 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).en
dc.referencesKhuntia, 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.2022.2115410en
dc.referencesKim, 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/10.1145/3497623.3497664en
dc.referencesKoga, 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.2023.0738en
dc.referencesKoleva, 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.2018.03.004en
dc.referencesKoshiyama, 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/10.1080/14697688.2020.1790635en
dc.referencesLeso, 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/s11846-023-00692-3en
dc.referencesLopez-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.2020.01.038en
dc.referencesLuo, 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.4071504en
dc.referencesManahov, 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).en
dc.referencesMishra, S. (2023), Exploring the Impact of AI-Based Cyber Security Financial Sector Management. Applied Sciences 13, 5875. https://doi.org/10.3390/app13105875en
dc.referencesMontemayor, 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-zen
dc.referencesMungoli, N. (2023), Revolutionizing Industries: The Impact of Artificial Intelligence Technologies. https://doi.org/10.11648/j.ajai.20220601.01en
dc.referencesNeupane, 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.2306.13033en
dc.referencesOgunpola, 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/diagnostics14020144en
dc.referencesQirui 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).en
dc.referencesRaju, 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/18.2.05en
dc.referencesRakha, 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.23en
dc.referencesSachan, 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).en
dc.referencesSadok, H., Sakka, F., El Maknouzi, M. (2022), Artificial intelligence and bank credit analysis: A review. Cogent Economics & Finance 10. https://doi.org/10.1080/23322039.2021.2023262en
dc.referencesSahare, P. (2023), InvestAI: Connecting With Future Gains. IJRASET 11, 2054–2057. https://doi.org/10.22214/ijraset.2023.57018en
dc.referencesShelf. (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).en
dc.referencesShilpa N S, Ms. (2023), Chatbot for MindTech Digital Solutions. IJRASET 11, 1534–1537. https://doi.org/10.22214/ijraset.2023.57672en
dc.referencesSingh, A., Ahlawat, N. (2023), A Review Article: The Growing Role Of Data Science And Ai In Banking And Finance. Open Access 05.en
dc.referencesSingh, D.N., Ahuja, D.S. (2024), Artificial Intelligence (AI) and Business. Kitab writing publication.en
dc.referencesStrobelt, 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.2021.3114845en
dc.referencesSun, 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/10.1145/3490099.3511119en
dc.referencesTakyar, 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).en
dc.referencesTiezzi, 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.2406.09062en
dc.referencesWang, M., Fu, W., He, X., Hao, S., Wu, X. (2020), A Survey on Large-scale Machine Learning. https://doi.org/10.48550/arXiv.2008.03911en
dc.referencesXu, 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.2020.03.005en
dc.referencesYuanming 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).en
dc.referencesZhai, 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/s40561-024-00316-7en
dc.referencesZhang, 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.2022.3232543en
dc.referencesZhang, 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.2022.127169en
dc.referencesZhou, 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.en
dc.referencesZohuri, 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/1050en
dc.contributor.authorEmailBayraktar, Dorin - dorin.bayraktar@student.uaic.ro
dc.contributor.authorEmailStoica, Eduard Alexandru - eduard.stoica@ulbsibiu.ro
dc.contributor.authorEmailBogoslov, Ioana Andreea - andreea.bogoslov@ulbsibiu.ro
dc.contributor.authorEmailGeorgescu, Radu Mircea - mirceag@uaic.ro
dc.identifier.doi10.18778/2082-4440.47.03


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