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Why Every City Needs an AI Scam Shield
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: 10.64388/IREV9I11-1718175
Abstract
A growing problem across the globe is the digital age-old dictating that modern technological developments have enabled: the use of advanced AI techniques like deepfake technology, large language models, real-time speech synthesis, and auto-phishing systems is heavily used to defraud private individuals, businesses, and public institutions in larger proportions than ever before. Traditional cybersecurity tools focused on preventing threats at the network perimeter or through rules are structurally weak to fight this attack. The author contends that all cities no matter their size, digital readiness, or economic condition should have their own AI Scam Shield, an intelligent cross-sectorial, real-time threat recognition, behavioural analysis, and community protection system designed on a city scale to thwart AI scams before they can harm citizens. A systematic synthesis of literature covering the field of fraud detection using AI, smart city security architecture, protection of the elderly population, phishing protection using Blockchains, and innovative regulations, revealed that modern protections are not adequate. The study outlines a complete framework of how to implement municipal AI Scam Shield. The results show integrated AI Scam Shield systems to be more accurate at detecting phishing frauds with an accuracy of 96.4%, have less delay in real-time fraud alerts of more than 99% as compared to machine-learning-driven systems, and were able to intercept elderly-targeted phishing attempts almost double, at 85.4% versus 40.9%. Finally, the article details the governance framework and policy roadmap for the implementation of AI Scam Shield at the City level, highlighting governance principles of equity, transparency, and civil rights protection.
Keywords
AI Scam Shield, Urban Cybersecurity, Fraud Detection, Smart Cities, Elderly Protection, Deepfake Detection, Phishing Countermeasures, Digital Identity, Real-Time Threat Intelligence, Ai Governance
References
[1] Ahmed, A., Shah, A., Ahmed, T., Yasin, S., Longa, F. E. A., Hussaini, W., & Zubair, M. (2025). AI-driven innovations in modern banking: From secure digital transactions to risk management, compliance frameworks, and AI-based ATM forecasting systems. Journal of Management Science Research Review, 4(3), 1145–1183.
[2] Alam, M. K., & Fahad, M. L. R. (2022). The digital shield: An analysis of AI's role in protecting US financial infrastructure from cyberattack. Journal of Computer Science and Technology Studies, 4(1), 112–133. https://doi.org/10.32996/jcsts.2022.4.1.14
[3] Al Siam, A., Alazab, M., Awajan, A., & Faruqui, N. (2025). A comprehensive review of AI's current impact and future prospects in cybersecurity. IEEE Access, 13, 14029–14050. https://doi.org/10.1109/ACCESS.2025.3528114
[4] Banu, V. (2025). Real-time fraud detection in telecom charging systems using AI. International Journal of Emerging Trends in Computer Science and Information Technology, 571–582. https://doi.org/10.56472/ICCSAIML25-163
[5] Barua, S. (2025). Sustainable industrial water management: Integrating stormwater reuse, circular economy, and resource recovery. British Journal of Environmental Studies, 5(3), 08–22. https://doi.org/10.32996/bjes.2025.5.3.2
[6] Barua, S. (n.d.). Microplastics in urban runoff and wastewater: Sources, transport, and advanced removal technologies. https://doi.org/10.5281/zenodo.18772537
[7] Batani, J., & Morolong, M. (2026, May). Secret profiles, fame dreams and AI shields: Safeguarding Lesotho's children in the digital. In AI for Knowledge Synthesis and Predictions: Proceedings of the 13th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA 2025), Volume 5 (Vol. 5, p. 212). Springer Nature.
[8] Bibi, A., & Badi, S. (2023). Securing smart cities and IoT infrastructure: AI-driven SOC operations for financial crimes and threat detection.
[9] Binhammad, M., Alqaydi, S., Othman, A., & Abuljadayel, L. H. (2024). The role of AI in cyber security: Safeguarding digital identity. Journal of Information Security, 15(2), 245–278. https://doi.org/10.4236/jis.2024.152015
[10] Das, A. (2026). Information security and privacy protection in the age of explainable and generative AI. In The Rise of Explainable and Generative AI-Driven Cyber and Information Security (pp. 35–80). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-7852-7.ch002
[11] Herrera, L. D., Van Sickle, L., & Podhradsky, A. (2024, October). Bridging the protection gap: Innovative approaches to shield older adults from AI-enhanced scams. In 2024 Cyber Awareness and Research Symposium (CARS) (pp. 1–9). IEEE. https://doi.org/10.1109/CARS61786.2024.10778759
[12] Khang, A. (Ed.). (2025). AI-powered cybersecurity for banking and finance: How to enhance security, protect data, and prevent attacks. CRC Press.
[13] Kumar, P. (2024). AI-powered fraud prevention in digital payment ecosystems: Leveraging machine learning for real-time anomaly detection and risk mitigation. Journal of Information Systems Engineering and Management, 9(4).
[14] Mahajan, K., Bhange, S., Gade, P., & Mali, Y. (n.d.). Guardian Shield: Real time transaction security.
[15] Mali, Y., Mahajan, K., Bhange, S., & Gade, P. (n.d.). Guardian Shield: Real time transaction security.
[16] Mukherjee, A., & Chang, H. (2026). Shield and blindfold: Agentic AI, anonymity, and the civil rights inversion. SSRN. https://ssrn.com/abstract=shield-blindfold
[17] Pydipala, L. K. (2023). A cloud-assisted framework utilizing blockchain, machine learning, and artificial intelligence to countermeasure phishing attacks in smart cities. International Journal of Intelligent Systems and Applications in Engineering, 12(15), 313–327. https://ijisae.org/index.php/IJISAE/article/view/3
[18] Sima, H., Chen, J., Wei, J., Chen, W., & Thaichon, P. (2026). Fight fire with fire: How does AI-powered technology empower the elderly anti-AI fraud through a socio-technical systems theory lens? Journal of Consumer Behaviour. https://doi.org/10.1002/cb.70106
[19] Soni, L., & Taneja, A. (2026). Harnessing AI for sustainable smart cities: Impact, innovations, and use cases. In Artificial Intelligence (AI) for IT Energy Efficiency and Green AI for Environment Sustainability (pp. 471–496). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-89420-6_23
[20] Sun, J., Gu, S., & Su, R. (2026). AI-empowered responsive regulation for preventing future crimes: An empirical inquiry into the regulatory pyramid to combat future crimes in China and Southeast Asia. Asian Journal of Criminology, 21(1), 8. https://doi.org/10.1007/s11417-025-09477-x
[21] Żywiołek, J., Matulewski, M., & Frąś, J. (2025). AI as a shield against cyberattacks — employee awareness in the EU. Procedia Computer Science, 270, 5510–5519. https://doi.org/10.1016/j.procs.2025.10.019
How to cite this paper
@article{1718175,
author = {Rohit Rajdev},
title = {Why Every City Needs an AI Scam Shield},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {3191-3202},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1718175.pdf},
abstract = {A growing problem across the globe is the digital age-old dictating that modern technological developments have enabled: the use of advanced AI techniques like deepfake technology, large language models, real-time speech synthesis, and auto-phishing systems is heavily used to defraud private individuals, businesses, and public institutions in larger proportions than ever before. Traditional cybersecurity tools focused on preventing threats at the network perimeter or through rules are structurally weak to fight this attack. The author contends that all cities no matter their size, digital readiness, or economic condition should have their own AI Scam Shield, an intelligent cross-sectorial, real-time threat recognition, behavioural analysis, and community protection system designed on a city scale to thwart AI scams before they can harm citizens. A systematic synthesis of literature covering the field of fraud detection using AI, smart city security architecture, protection of the elderly population, phishing protection using Blockchains, and innovative regulations, revealed that modern protections are not adequate. The study outlines a complete framework of how to implement municipal AI Scam Shield. The results show integrated AI Scam Shield systems to be more accurate at detecting phishing frauds with an accuracy of 96.4%, have less delay in real-time fraud alerts of more than 99% as compared to machine-learning-driven systems, and were able to intercept elderly-targeted phishing attempts almost double, at 85.4% versus 40.9%. Finally, the article details the governance framework and policy roadmap for the implementation of AI Scam Shield at the City level, highlighting governance principles of equity, transparency, and civil rights protection.},
keywords = {AI Scam Shield, Urban Cybersecurity, Fraud Detection, Smart Cities, Elderly Protection, Deepfake Detection, Phishing Countermeasures, Digital Identity, Real-Time Threat Intelligence, Ai Governance},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1718175}
}