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TELEFON DOLANDIRICILIKLARI VE KARŞI ÖNLEMLERDE YAPAY ZEKA ARAÇLARININ KULLANIMI

Year 2025, Volume: 5 Issue: 2, 223 - 246
https://doi.org/10.71284/jpepfm.202527

Abstract

Yapay zeka teknolojilerinin kullanımı telefon dolandırıcılıklarına yeni ve daha sofistike bir boyut kazandırdı. Bu teknolojiler dolandırıcıların kurbanlarını daha etkili bir şekilde hedeflemelerine olanak sağlıyor. Bu makale, dolandırıcılıkta kurban profili, duygu analizi, ses taklit teknolojileri ve yapay zeka destekli konuşma yöntemleri üzerindeki yapay zekanın etkisini inceliyor. Yapay zeka algoritmaları, sosyal medya ve dijital izler aracılığıyla kişileri profilleyerek hedeflemeyi daha hassas hale getirebilir.
Duygu analizi, dolandırıcılık mesajlarının içeriğini analiz ederek ve stratejileri gerçek zamanlı olarak uyarlayarak dolandırıcılık niyetlerini tespit edebilir. Ses taklit teknolojileri, dolandırıcıların kurbanlarını güvenilir sesler kullanarak manipüle etmelerine olanak tanır. Yapay zeka destekli konuşma sistemleri, dolandırıcılık senaryolarını daha ikna edici ve etkili hale getirir.
Bu gelişmeler, dolandırıcılık tespiti ve önleme stratejilerinin evrimini gerekli kılıyor. Yapay zeka teknolojilerinin kötüye kullanılmasını önlemek için dolandırıcılık filtreleri ve yapay zeka tespit uygulamaları geliştirilmelidir. Ayrıca, akıllı telefonlar için yapay zeka destekli uygulamaların geliştirilmesi, bireylerin ve kurumların dolandırıcılık girişimlerine karşı daha hazırlıklı olmalarına yardımcı olabilir. Bu tür uygulamalar, gerçek zamanlı duygu analizi, ses taklidi tespiti ve profil analizi gibi özellikler sunarak dolandırıcılığı daha etkili bir şekilde tespit edebilir.
AI ürünlerinin etik ve sorumlu bir şekilde kullanılmasını sağlayarak dijital dünyadaki güvenlik artırılabilir. Geliştirilecek bu tür sistemler, dolandırıcılık girişimlerini erken bir aşamada tespit ederek kullanıcıları koruyacak ve dijital dünyaya olan güveni artıracaktır.

References

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  • Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014
  • Sangwan, R. S., Badr, Y., & Srinivasan, S. M. (2023). Cybersecurity for AI systems: A survey. Journal of Cybersecurity and Privacy, 3(2), 166-190. https://doi.org/10.3390/jcp3020010
  • Shaukat, K., Luo, S., Chen, S., & Liu, D. (2020). Cyber threat detection using machine learning techniques: A performance evaluation perspective. 2020 International Conference on Cyber Warfare and Security (ICCWS), 1-6. https://doi.org/10.1109/ICCWS48432.2020.9292388
  • Subex. (2024). Harnessing generative AI and AI agents to tackle modern telecom fraud challenges. https://www.subex.com/ai/

USE OF ARTIFICIAL INTELLIGENCE TOOLS FOR TELEPHONE SCAMS AND COUNTERMEASURES

Year 2025, Volume: 5 Issue: 2, 223 - 246
https://doi.org/10.71284/jpepfm.202527

Abstract

The use of artificial intelligence technologies has brought a new and more sophisticated dimension to telephone scams. These technologies allow scammers to target their victims more effectively. This article examines the impact of AI on victim profiling, sentiment analysis, voice mimicry technologies, and AI-assisted conversation methods in fraud. AI algorithms can make targeting more precise by profiling individuals through social media and digital traces.
Sentiment analysis can detect fraudulent intentions by analysing the content of scam messages and adapt strategies in real-time. Voice mimicry technologies allow scammers to manipulate their victims using trustworthy voices. AI-assisted conversation systems make fraud scenarios more convincing and effective.
These developments necessitate the evolution of fraud detection and prevention strategies. Fraud filters and AI detection applications should be developed to prevent the misuse of AI technologies. In addition, the development of AI-assisted applications for smartphones can help individuals and institutions be more prepared against fraud attempts. Such applications can more effectively detect fraud by offering features such as real-time sentiment analysis, voice mimicry detection, and profile analysis.
By ensuring the ethical and responsible use of AI products, security in the digital world can be increased. These types of systems, which will be developed, will protect users by detecting fraud attempts at an early stage and will increase trust in the digital world.

References

  • American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000
  • Alzahrani, R. A., & Aljabri, M. (2023). AI-based techniques for ad click fraud detection and prevention: Review and research directions. Journal of Sensor and Actuator Networks, 12(1), 4. https://doi.org/10.3390/jsan12010004
  • Al-Khazaali, A., Shakir, M., & Abdallah, S. (2023). AI-based model for fraud detection in bank systems. ResearchGate. https://www.researchgate.net/publication/376388638 AIbased model for fraud detection in bank systems
  • Babu, P. B., Aswini, T. N., Vishnu, M. H. S., Gopiprashanth, B., & Reddyjanapala, B. S. (2024). Detecting and classifying fraudulent SMS and email with a robust machine learning approach. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(1), 109-112. https://doi.org/10.61841/turcomat.v15i1.14549
  • Bhargavi, D. K., & Shivani, B. M. (2024). Detection of fraudulent phone calls detection in mobile applications. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(2), 1-5. https://doi.org/10.61841/turcomat.v15i2.14644
  • Blauth, T. F., Gstrein, O. J., & Zwitter, A. (2022). Artificial intelligence crime: An overview of malicious use and abuse of AI. IEEE Access, https://ieeexplore.ieee.org/abstract/document/9831441
  • Chandran, D. R. (2022). Use of AI voice authentication technology instead of traditional keypads in security devices. Journal of Computer and Communications, 10(6), 11-21. https://doi.org/10.4236/jcc.2022.106002
  • Çalışkan, M. (2018). Yeni nesil siber saldırılar ve korunma yolları. Journal of Polytechnic, 21(3), 733-741. https://dergipark.org.tr/tr/download/article-file/747841
  • Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55, 591-621. https://doi.org/10.1146/annurev.psych.55.090902.142015
  • Cialdini, R. B. (2006). Influence: The psychology of persuasion (Rev. ed.). Harper Business.
  • European Public Prosecutor’s Office. (2021). Aggravated customs fraud in Germany, Austria and Slovakia: Damage of more than €1.1 million to the EU budget. Retrieved June 16, 2024, from https://www.eppo.europa.eu/en/media/news/aggravated-customs-fraud-germany-austria-and-slovakia-damage-more-eu11-million-to-eu
  • FightCybercrime.org. (2023). The rise of AI in phishing scams: How scammers use it and how we can fight back. FightCybercrime.org. Retrieved from https://www.fightcybercrime.org/blog/the-rise-of-ai-in-phishing-scams/ (Accessed by VPN and membership method)
  • Financial Fraud Consortium. (2024). Fraud prevention and mitigation resources. https://www.fraudconsortium.org/
  • Global Engagement. IRS scams. Texas A&M University. Retrieved June 16, 2024, from https://global.tamu.edu/isss/resources/taxes/irs-scams
  • Gouldner, A. W. (1960). The norm of reciprocity: A preliminary statement. https://www.jstor.org/stable/2092623 IdentityIQ. (2023). The rise of AI social engineering scams. IdentityIQ. Retrieved from https://www.identityiq.com/blog/the-rise-of-ai-social-engineering-scams/
  • Insights2Techinfo. (2024). Unmasking scam calls: Analyzing and detecting scammers using AI. Insights2Techinfo. https://insights2techinfo.com
  • Liu, J. (2021). Understanding and defending against telephone scams with large-scale data analytics and machine learning systems (Doctoral dissertation, University of Georgia). ProQuest Dissertations & Theses. https://www.proquest.com/openview/35085b2b8c0c312c0da2a24b97b9de18/1?pq-origsite=gscholar&cbl=18750&diss=y
  • Milani, A., Petrocchi, M., Pietro, R. D., & Spognardi, A. (2024). Chatbot-based emotional intelligence: A systematic review and new directions. Computers, 13(1), 5. https://doi.org/10.3390/computers13010005
  • Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014
  • Sangwan, R. S., Badr, Y., & Srinivasan, S. M. (2023). Cybersecurity for AI systems: A survey. Journal of Cybersecurity and Privacy, 3(2), 166-190. https://doi.org/10.3390/jcp3020010
  • Shaukat, K., Luo, S., Chen, S., & Liu, D. (2020). Cyber threat detection using machine learning techniques: A performance evaluation perspective. 2020 International Conference on Cyber Warfare and Security (ICCWS), 1-6. https://doi.org/10.1109/ICCWS48432.2020.9292388
  • Subex. (2024). Harnessing generative AI and AI agents to tackle modern telecom fraud challenges. https://www.subex.com/ai/
There are 22 citations in total.

Details

Primary Language English
Subjects Development of Science, Technology and Engineering Education and Programs
Journal Section Research Articles
Authors

Hakan Yıldırım 0000-0002-5959-2691

Yeşim Bütüner 0009-0000-9170-5097

Cihan Ünal 0000-0002-5255-4078

Early Pub Date October 18, 2025
Publication Date October 21, 2025
Submission Date March 4, 2025
Acceptance Date April 30, 2025
Published in Issue Year 2025 Volume: 5 Issue: 2

Cite

APA Yıldırım, H., Bütüner, Y., & Ünal, C. (2025). USE OF ARTIFICIAL INTELLIGENCE TOOLS FOR TELEPHONE SCAMS AND COUNTERMEASURES. Journal of Public Economy and Public Financıal Management, 5(2), 223-246. https://doi.org/10.71284/jpepfm.202527