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

Authors

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

Keywords:

GenAI, financial services, automation, operational efficiency, AI ethics, data privacy

Abstract

In 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.

References

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
Google Scholar 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
Google Scholar 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.
Google Scholar

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
Google Scholar 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
Google Scholar 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).
Google Scholar

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
Google Scholar 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
Google Scholar 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.
Google Scholar 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
Google Scholar 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
Google Scholar 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).
Google Scholar 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
Google Scholar 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
Google Scholar

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
Google Scholar 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
Google Scholar 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
Google Scholar 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
Google Scholar 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
Google Scholar 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
Google Scholar 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).
Google Scholar

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
Google Scholar 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.
Google Scholar

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

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
Google Scholar 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
Google Scholar 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
Google Scholar DOI: https://doi.org/10.1109/ICTAI59109.2023.00073

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

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

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
Google Scholar 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
Google Scholar 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
Google Scholar 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
Google Scholar 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
Google Scholar 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
Google Scholar 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
Google Scholar 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
Google Scholar 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).
Google Scholar 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
Google Scholar 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
Google Scholar 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
Google Scholar

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
Google Scholar

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
Google Scholar 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).
Google Scholar

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
Google Scholar 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
Google Scholar 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).
Google Scholar

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
Google Scholar 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
Google Scholar 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).
Google Scholar

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

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

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
Google Scholar 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
Google Scholar 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).
Google Scholar

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
Google Scholar

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
Google Scholar 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
Google Scholar 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).
Google Scholar

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
Google Scholar 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
Google Scholar 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
Google Scholar 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.
Google Scholar 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
Google Scholar DOI: https://doi.org/10.54026/CTES/1050