Scamshield – Catch Scammers with Autorecorded Calls: Review Paper

The rise of digital communication has been accompanied by a significant increase in fraudulent and scam phone calls, causing both financial and emotional damage to individuals and organizations. Traditional rule-based detection systems have become insufficient in combating these evolving scams, which often rely on voice manipulation and social engineering techniques. This review paper explores recent research and technological advancements in artificial intelligence (AI), voice biometrics, and data mining methods for scam and fraud call detection. It surveys existing literature in the domain of telecommunication fraud prevention, highlighting methods such as machine learning–based classification, real-time voice recognition, and behavioral pattern analysis. The proposed system, ScamShield, integrates these ideas to create a lightweight Android-based application capable of identifying suspicious calls through voice and keyword analysis, storing call metadata, and providing scam-awareness alerts. The review aims to bridge existing research gaps by summarizing multiple approaches to telecommunication fraud detection and offering insights for future AI-powered solutions that ensure secure and trustworthy communication networks.

Telecommunication Fraud, Scam Call Detection, Artificial Intelligence, Voice Recognition, Machine Learning

1. Khalid Hafiz Mir, Ravin Kumar, and Sangeeta Rai, “Real-Time Anomaly Detection in Telecommunications: Advanced Data Mining Techniques for Fraud Identification,” IEEE Conference on Data Science and Communication Systems, 2023. [Google Scholar] [Crossref]

2. Saloni Malhotra, Ginni Arora, and Ruchika Bathla, “Detection and Analysis of Fraud Phone Calls using Artificial Intelligence,” IEEE ICACCS, May 2023. [Google Scholar] [Crossref]

3. Batoul Abo Yehya and Nazih Salhab, “Telecommunications Fraud Machine Learning-based Detection,” IEEE ICAISC, Oct. 2023. [Google Scholar] [Crossref]

4. Ritika H. J. and Mohana, “Fraud Detection and Management for Telecommunication Systems using Artificial Intelligence (AI),” IEEE ICCCIS, Oct. 2022. [Google Scholar] [Crossref]

5. Sameer Qayyum et al., “Fraudulent Call Detection for Mobile Networks,” IEEE ICIET, June 2010. [Google Scholar] [Crossref]

6. Osama Mohamed Elrajubi et al., “Detection of Bypass Fraud Based on Speaker Recognition,” IEEE International Conference on Advanced Computing, May 2017. [Google Scholar] [Crossref]

7. Rushikesh Sonwane et al., “TrustCaller – Voice-based Fraud Prevention System,” IEEE ICACCS, June 2024. [Google Scholar] [Crossref]

8. Fawcett, T. and Provost, F., “Adaptive Fraud Detection,” Data Mining and Knowledge Discovery, 1(3), 291–316, 1997. [Google Scholar] [Crossref]

9. H. Weng et al., “Online E-Commerce Fraud: A LargeScale Detection and Analysis,” IEEE ICDE, 2018. [Google Scholar] [Crossref]

10. S. M. Gowri et al., “Detection of Telephony Spam and Scams using Recurrent Neural Network (RNN) Algorithm,” IEEE ICACCS, 2021. [Google Scholar] [Crossref]

11. Abidogun, O. A., “Data Mining, Fraud Detection and Mobile Telecommunications: Call Pattern Analysis with Unsupervised Neural Networks,” University of the Western Cape Dissertation, 2005. [Google Scholar] [Crossref]

12. S. Sandhya, N. Karthikeyan, and R. Sruthi, “Machine Learning Method for Detecting and Analysis of Fraud Phone Calls Datasets,” IJRTE, 2020. [Google Scholar] [Crossref]

13. Crawford, M. et al., “Survey of Review Spam Detection using Machine Learning Techniques,” Journal of Big Data, 2015. [Google Scholar] [Crossref]

14. A. Marzuoli et al., “Uncovering the Landscape of Fraud and Spam in the Telephony Channel,” IEEE ICMLA, 2016. [Google Scholar] [Crossref]

15. Luis Cortesao et al., “Fraud Management Systems in Telecommunications: A Practical Approach,” CiteseerX Journal, 2018. [Google Scholar] [Crossref]

16. Qianqian Zhao et al., “Detecting Telecommunication Fraud by Understanding the Contents of a Call,” Cybersecurity Journal, 2018. [Google Scholar] [Crossref]

17. Kasra Babaei et al., “A Study of Fraud Types, Challenges, and Detection Approaches in Telecommunication,” Journal of Information Systems and Telecommunication, 2019. [Google Scholar] [Crossref]

18. Richard A. Becker et al., “Fraud Detection in Telecommunications: History and Lessons Learned,” Technometrics, 2010. [Google Scholar] [Crossref]

19. Y. Moreau et al., “A Hybrid System for Fraud Detection in Mobile Communications,” ESANN Proceedings, 1999. [Google Scholar] [Crossref]

20. P. Ferreira et al., “Establishing Fraud Detection Patterns Based on Signatures,” Industrial Conference on Data Mining, 2006. [Google Scholar] [Crossref]

21. Alsaify, Baha et al., “Voice-Based Human Identification Using Machine Learning,” IEEE ICICS, 2022. [Google Scholar] [Crossref]

22. Khan M.K. Amjad and Aithal, S., “Voice Biometric Systems for User Identification and Authentication – A Literature Review,” IJAEML, 2022. [Google Scholar] [Crossref]

23. Dhakal, P. et al., “A Near Real-Time Automatic Speaker Recognition Architecture for Voice-Based User Interface,” Machine Learning Applications Journal, 2019. [Google Scholar] [Crossref]

24. K. S. G. et al., “Voice Comparison Approaches for Forensic Application: A Review,” IEEE ICSCCC, 2023. [Google Scholar] [Crossref]

25. Z. K. Abdul and A. K. Al-Talabani, “Mel Frequency Cepstral Coefficient and Its Applications: A Review,” IEEE Access, 2022. [Google Scholar] [Crossref]

26. Nishtha Tandel et al., “Voice Recognition and Voice Comparison using Machine Learning Techniques: A Survey,” IEEE ICACCS, 2020. [Google Scholar] [Crossref]

27. S. Kinkiri and S. Keates, “Speaker Identification: Variations of a Human Voice,” IEEE ICACCE, 2020. [Google Scholar] [Crossref]

28. H. Kilinc, “A Case Study on Fraudulent User Behaviors in the Telecommunication Network,” Electrica Journal, 2021. [Google Scholar] [Crossref]

29. Y. Alraouji and A. Bramantoro, “International Call Fraud Detection Systems and Techniques,” MEDES Conference, 2014. [Google Scholar] [Crossref]

30. M. Sahin and A. Francillon, “Understanding and Detecting International Revenue Share Fraud,” Network and Distributed System Security Symposium, 2021. [Google Scholar] [Crossref]

• Performance Analysis of Silicon PV Array Using Infrared Thermography and Detecting Temperature Non-Uniformities
• Dynamic Capabilities and Digital Transformation Performance: The Role of Organizational Agility in the Public Sector
• Comparative Effectiveness of Neural Mobilization Versus Positional Release Technique in Cervical Radiculopathy – An Experimental Study
• Soil Moisture Mapping of Solano Nueva Vizcaya: A Comparison and Review
• Silent Assistance for Emergencies (SAFE): A Mobile-Based Emergency Reporting Application for the Deaf and Mute Community