A REVIEW ON EXISTING METHODS OF FRAUD DETECTION IN MESSENGERS
Abstract
The increasing number of messenger fraud cases requires early and precise threat detection at unprecedented levels. The research examines modern NLP-based approaches which detect deceptive messages in messaging applications. The research examines various NLP approaches which analyze text data from different messaging platforms through text classification and tonality analysis and anomaly detection and thematic modeling techniques. The paper examines model learning data types together with text pre-processing methods and essential text features and evaluates traditional methods (e.g., Bag of Words, TF-IDF) and modern neural networks. The researchers encounter multiple obstacles while working which include the complex nature of processing informal language and the presence of noisy data and the need to frequently update models to detect new fraudulent schemes.
The research focuses on messenger platform fraud detection because it addresses the unique challenges of real-time message streams and informal language and multimodal communication. The review evaluates technical and contextual aspects by presenting suitable models and architectures for dynamic short-form content and identifying technologies that deliver low-latency responses.
The research aims to assess existing methods while identifying optimal approaches and proposing new directions to boost the accuracy and reliability of messenger fraud detection systems.
References
İli, B. (2025). Analysis of Complaints Regarding Cryptocurrency Investment Fraud: An Evaluation from the Perspective of New Media Literacy. Iğdır Üniversitesi Sosyal Bilimler Dergisi, (38), 214-229.
Europol. (2024). Internet organised crime threat assessment (IOCTA 2024). Publications Office of the European Union.
Chaganti, R., Bhushan, B., Nayyar, A., & Mourade, A. (2021). Recent trends in social engineering scams and case study of gift card scam. arXiv preprint arXiv:2110.06487.
Chetioui, K., Bah, B., Alami, A. O., & Bahnasse, A. (2022). Overview of social engineering attacks on social networks. Procedia Computer Science, 198, 656-661.
Siddiqi, M. A., Pak, W., & Siddiqi, M. A. (2022). A study on the psychology of social engineering-based cyberattacks and existing countermeasures. Applied Sciences, 12(12), 6042.
Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: a review of anomaly detection techniques and recent advances. Expert systems With applications, 193, 116429.
Emran, A. K. M., & Rubel, M. T. H. (2024). Big data analytics and ai-driven solutions for financial fraud detection: Techniques, applications, and challenges. Innovatech Engineering Journal, 1(01), 10-70937.
Bello, O. A., Folorunso, A., Ejiofor, O. E., Budale, F. Z., Adebayo, K., & Babatunde, O. A. (2023). Machine learning approaches for enhancing fraud prevention in financial transactions. International Journal of Management Technology, 10(1), 85-108.
Bello, O. A., & Olufemi, K. (2024). Artificial intelligence in fraud prevention: Exploring techniques and applications challenges and opportunities. Computer science & IT research journal, 5(6), 1505-1520.
Boulieris, P., Pavlopoulos, J., Xenos, A., & Vassalos, V. (2024). Fraud detection with natural language processing. Machine Learning, 113(8), 5087-5108.
Oyeyemi, D. A., & Ojo, A. K. (2024). SMS Spam Detection and Classification to Combat Abuse in Telephone Networks Using Natural Language Processing. arXiv preprint arXiv:2406.06578.
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2022). A systematic literature review on phishing email detection using natural language processing techniques. IEEE Access, 10, 65703-65727.
Gupta, A. (2024, April). Detection of Spam and Fraudulent calls Using Natural Language Processing Model. In 2024 Sixth International Conference on Computational Intelligence and Communication Technologies (CCICT) (pp. 423-427). IEEE.
Saloot, M. A., & Pham, D. N. (2021). Real-time text stream processing: A dynamic and distributed NLP pipeline. In Proceedings of the 2021 International Symposium on Electrical, Electronics and Information Engineering (ISEEIE) (pp. 575–584). ACM.
Boulieris, P., Pavlopoulos, J., Xenos, A., & Vassalos, V. (2024). Fraud detection with natural language processing. Artificial Intelligence Review, 113, 5087–5108.
Carter, E. (2021). Distort, extort, deceive and exploit: exploring the inner workings of a romance fraud. The British Journal of Criminology, 61(2), 283-302.
Pinjarkar, L., Hete, P. R., Mattada, M., Nejakar, S., Agrawal, P., & Kaur, G. (2024, July). An Examination of Prevalent Online Scams: Phishing Attacks, Banking Frauds, and E-Commerce Deceptions. In 2024 Second International Conference on Advances in Information Technology (ICAIT) (Vol. 1, pp. 1-6). IEEE.
Mat Ridzuan, N. I., Said, J., Razali, F. M., Abdul Manan, D. I., & Sulaiman, N. (2022). Examining the role of personality traits, digital technology skills and competency on the effectiveness of fraud risk assessment among external auditors. Journal of Risk and Financial Management, 15(11), 536.
Mittal, R., Singh, S. K., Kumar, S., Khullar, T., Kumar, R., Gupta, B. B., & Psannis, K. (2025). Advanced Techniques and Best Practices for Phishing Detection. In Critical Phishing Defense Strategies and Digital Asset Protection (pp. 149-186). IGI Global Scientific Publishing.
Rahmatdildaevna Kurmanbekova, Z., Sarekenova, K. K., Oner, M., Turarbekovich Malikov, K., & Sagatovna Shokabayeva, S. (2023). A linguistic analysis of social network communication. International Journal of Society, Culture & Language, 11(1), 119-132.
Kydros, D., Pazarskis, M., & Karakitsiou, A. (2022). A framework for identifying the falsified financial statements using network textual analysis: a general model and the Greek example. Annals of Operations Research, 316(1), 513-527.
Shang, Y., Wang, K., Tian, Y., Zhou, Y., Ma, B., & Liu, S. (2023). Theoretical basis and occurrence of internet fraud victimisation: Based on two systems in decision-making and reasoning. Frontiers in Psychology, 14, 1087463.
Soares, G. F., & Ramamurthy, I. P. (2022). A Comparison of Machine Learning and Deep Learning Models with Advanced Word Embeddings: The Case of Internal Audit Reports. Optimization and Machine Learning: Optimization for Machine Learning and Machine Learning for Optimization, 151.
Bansal, M., Goyal, A., & Choudhary, A. (2022). A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decision Analytics Journal, 3, 100071.
Bello, H. O., Ige, A. B., & Ameyaw, M. N. (2024). Deep learning in high-frequency trading: conceptual challenges and solutions for real-time fraud detection. World Journal of Advanced Engineering Technology and Sciences, 12(02), 035-046.
Bo, S., Zhang, Y., Huang, J., Liu, S., Chen, Z., & Li, Z. (2024, August). Attention mechanism and context modeling system for text mining machine translation. In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS) (pp. 857-863). IEEE.
Vyas, S., Tyagi, R. K., Jain, C., & Sahu, S. (2021, July). Literature review: A comparative study of real time streaming technologies and apache kafka. In 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT) (pp. 146-153). IEEE.
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2021). Phishing email detection using natural language processing techniques: a literature survey. Procedia Computer Science, 189, 19-28.
Kotagiri, A., & Yada, A. (2024). Crafting a strong anti-fraud defense: RPA, ML, and NLP collaboration for resilience in US finance. International Journal of Management Education for Sustainable Development, 7(7), 1–5.
Ukwen, D. O., & Karabatak, M. (2021, June). Review of NLP-based systems in digital forensics and cybersecurity. In 2021 9th International symposium on digital forensics and security (ISDFS) (pp. 1-9). IEEE.
Rodríguez, J. F., Papale, M., Carminati, M., & Zanero, S. (2022). A natural language processing approach for financial fraud detection. In CEUR workshop proceedings (Vol. 3260, pp. 135-149). CEUR-WS. org.
Msowoya, P., & Tawarish, T. (2024). Fraud detection: Combating mobile money fraud in SMS messages using machine learning. International Journal of Emerging Trends in Science and Technology, 11(7), 8077–8083.
Verma, S., Ayala-Rivera, V., & Portillo-Dominguez, A. O. (2023, November). Detection of Phishing in Mobile Instant Messaging Using Natural Language Processing and Machine Learning. In 2023 11th International Conference in Software Engineering Research and Innovation (CONISOFT) (pp. 159-168). IEEE.
Verma, P., Goyal, A., & Gigras, Y. (2020). Email phishing: Text classification using natural language processing. Computer Science and Information Technologies, 1(1), 1–12.
Adekanmbi, O., Onilude, G., & Olabiyi, A. SMS Fraud Detector and Instant Fraud Prevention Call Alert.
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Copyright (c) 2025 Maxim Zheludkov, Aisultan Shoiynbek, Karim Sharipov, Azamat Serek, Temirlan Shoiynbek, Darkhan Kuanyshbay, Bakhtiyor Meraliyev
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