Artificial Intelligence in Security: Driving Trust and Customer Engagement on FX Trading Platforms
DOI:
https://doi.org/10.60087/jklst.v4.n1.008Abstract
The study aimed to examine how artificial intelligence (AI)-powered security systems enhance customer trust and engagement in Forex (FX) Trading platforms, the study also mark the effectiveness of AI technologies in mitigating security threats on FX platforms, and explore the role of AI in ensuring regulatory compliance and transparency, thereby fostering a more secure trading environment. The study used both qualitative and quantitative data. Empirical in nature, the study focuses on the users of trading platforms engaged in Foreign Exchange (FX) dealing in the Delhi NCR region. The study uses descriptive and exploratory research design and provides a target population of 250 respondents. A structured questionnaire is employed as the main source of data collection to address the research questions. The data is analyzed by statistical tools such as MS Excel and SPSS, using mean, S.D., correlation, regression, etc. The study showed that there is a clear positive correlation between the level of incorporation of AI security systems and the level of customer engagement, with AI technologies accounting for an important share of the variation in security threat prevention. Also, the study confirmed the role of AI in compliance and openness, which exhibits a moderate positive relationship between them.
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Acikgoz, F., Perez‐Vega, R., Okumus, F., & Stylos, N. (2023). Consumer engagement with AI‐ powered voice assistants: A behavioral reasoning perspective. Psychology & Marketing, 40(11), 2226-2243.
Ahmed, F. (2024). Artificial Intelligence and its Impact on Customer Service: Enhancing Experiences or Eroding Trust. Review Journal for Management & Social Practices, 1(3), 10-19.
Balasubramaniam, N., Kauppinen, M., Rannisto, A., Hiekkanen, K., & Kujala, S. (2023). Transparency and ex-plainability of AI systems: From ethical guidelines to re-quirements. Information and Software Technology, 159, 107197.
Chawla, R. N., & Goyal, P. (2021). Emerging trends in digital transformation: a bibliometric analysis. Benchmarking: An International Journal, 29(4), 1069-1112.
Chirra, B. R. (2020). Securing Operational Technology: AI-Driven Strategies for Overcoming Cybersecurity Chal-lenges. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 281-302.
De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & Von Wangenheim, F. (2020). Artificial intelligence and mar-keting: Pitfalls and opportunities. Journal of Interactive Mar-keting, 51(1), 91-105.
Shiwlani, A., Kumar, S., Kumar, S., Hasan, S. U., & Shah, M. H. A. Transforming Healthcare Economics: Machine Learn-ing Impact on Cost Effectiveness and Value-Based Care.
Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., de Prado, M. L., Herrera-Viedma, E., & Herrera,
F. (2023). Connecting the dots in trustworthy Artificial Intel-ligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Information Fusion, 99, 101896.
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71.
Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., ... & Wang, Y. (2021). Setting the fu-ture of digital and social media marketing research: Perspec-tives and research propositions. International journal of in-formation management, 59, 102168.
Flavian, C., Guinaliu, M., & Torres, E. (2005). The influence of corporate image on consumer trust: A comparative analysis in traditional versus internet banking. Internet research, 15(4), 447-470.
Hlee, S., Park, J., Park, H., Koo, C., & Chang, Y. (2023). Un-derstanding customer's meaningful engagement with AI-powered service robots. Information Technology & People, 36(3), 1020-1047.
Kumar, S., Shiwlani, A., Hasan, S. U., Kumar, S., Shamsi, F., & Hasan, S. Artificial Intelligence in Organ Transplantation: A Systematic Review of Current Advances, Challenges, and Future Directions.
Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Con-sumer brand engagement in social media: Conceptualization, scale development and validation. Journal of interactive marketing, 28(2), 149-165.
https://pacson.org/partners/cybersecurity-and-infrastructure-security-agency- cisa#:~:text=The%20Cybersecurity%20and%20Infrastructure%20Security,resilient%20infrastructur e%20for%20the%20future.
Hu, Y., Kuang, W., Qin, Z., Li, K., Zhang, J., Gao, Y., ... & Li, K. (2021). Artificial intelligence security: Threats and coun-termeasures. ACM Computing Surveys (CSUR), 55(1), 1-36.
Kalluri, K. (2022). Federate Machine Learning: A Secure Paradigm for Collaborative AI in Privacy-Sensitive Do-mains. International Journal on Science and Technolo-gy, 13(4), 1-13.
Kaluarachchi, B. N., & Sedera, D. (2024). Improving Effi-ciency Through AI-Powered Customer Engagement by Providing Personalized Solutions in the Banking Industry. In Integrating AI-Driven Technologies into Service Marketing (pp. 299-342). IGI Global.
Kasaraneni, R. K. (2022). AI-powered Chatbots in Banking: Evaluating Performance, User Satisfaction, and Operational Efficiency. Journal of AI-Assisted Scientific Discovery, 2(1), 355-392.
Kiseleva, A., Kotzinos, D., & De Hert, P. (2022). Transparency of AI in healthcare as a multilayered system of accountabili-ties: between legal requirements and technical limitations. Frontiers in artificial intelligence, 5, 879603.
Kitirianglap, K. (2018). Conatu: Chiwit lae amnat chuapchum chiwit nai rabop thunniyom haeng satawat thi yisipet.
Kodete, C. S., Thuraka, B., Pasupuleti, V., & Malisetty, S. (2024). Determining the efficacy of machine learning strate-gies in quelling cyber security threats: Evidence from selected literatures. Asian Journal of Research in Computer Science, 17(8), 24-33.
Kalluri, K. (2024). AI-Driven Risk Assessment Model for Financial Fraud Detection: a Data Science Perspec-tive. International Journal of Scientific Research and Man-agement, 12(12), 1764-1774.
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how? International Journal of Information Management, 77, 102783.
Larsson, S., & Heintz, F. (2020). Transparency in artificial intelligence. Internet policy review, 9(2).
Leninkumar, V. (2017). The relationship between customer satisfaction and customer trust on customer loyalty. Interna-tional Journal of Academic Research in Business and Social Sciences, 7(4), 450-465.
McLean, G., Osei-Frimpong, K., & Barhorst, J. (2021). Alexa, do voice assistants influence consumer brand engagement?–Examining the role of AI powered voice assistants in influ-encing consumer brand engagement. Journal of Business Re-search, 124, 312-328.
Kalluri, K. (2024). Scalable fine-tunning strategies for llms in finance domain-specific application for credit union.
Moşteanu, N. R. (2023, June). Unleashing the potential of forex trading with intelligent automation and blockchain in-novation through a dynamic transformation. In Proceedings of the 31st RSEP International Conference on Economics, Fi-nance and Business (pp. 22-23).
Nguyen, N., Leclerc, A., & LeBlanc, G. (2013). The mediat-ing role of customer trust on customer loyalty.
Shiwlani, A., Hasan, S. U., & Kumar, S. (2024). Artificial Intelligence in Neuroeducation: A Systematic Review of AI Applications Aligned with Neuroscience Principles for Opti-mizing Learning Strategies. Journal of Development and So-cial Sciences, 5(4), 578-593.
Nimmagadda, V. S. P. (2021). Artificial Intelligence for Compliance and Regulatory Reporting in Banking: Advanced Techniques, Models, and Real-World Applications. Journal of Bioinformatics and Artificial Intelligence, 1(1), 151-189.
Thakur, R. (2016). Understanding customer engagement and loyalty: a case of mobile devices for shopping. Journal of Retailing and consumer Services, 32, 151-163.
Weng, Y., & Wu, J. (2024). Leveraging artificial intelligence to enhance data security and combat cyber-attacks. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 5(1), 392-399.
Wischmeyer, T. (2020). Artificial intelligence and transpar-ency: opening the black box. Regulating artificial intelligence, 75-101.
Xia, B. S., & Gong, P. (2014). Review of business intelligence through data analysis. Benchmarking: An International Jour-nal, 21(2), 300-311.
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