Machine Learning for Suspicious Behaviour Detection and Churn Prediction in Telecom Customer Call Data
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I3P108Keywords:
Artificial Intelligence, Fraud Phone Call Identification, Analysis, Call Center, Deep Learning, Customer Call Intent PredictionAbstract
Auto dealerships get tens of thousands of calls every day from people who want to buy, get repairs, sell, or find work. For car dealerships to get the most out of their customer service agents, ensure customer happiness through great experiences, and boost sales and revenue through deeper engagement with customers, it is critical to understand the purpose of all those calls. This paper introduces a framework that utilizes deep learning (DL) to analyze customer call records and identify questionable patterns of behavior, such as customer churn. The approach utilizes a structured machine learning (ML) pipeline to clean the data, handle missing values, eliminate duplicates and outliers, apply Min-Max scaling to normalize numerical features, and use one-hot encoding for categorical variables. 48 elements from three months' worth of call detail records make up the Telecom Customer Churn dataset. Training data makes up 80% of the processed data, whereas testing data is 20% of the total. It developed a Convolutional Neural Network (CNN) architecture to automatically extract hierarchical feature representations and identify intricate patterns in consumer behavior. Conventional ML models such as Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT) are significantly outperformed by the CNN, which achieves 96.5% accuracy, 91.7% precision, 99.8% recall, 95.6% F1-score, and 0.972 ROC-AUC. These outcomes validate the model's exceptional capability in both classification and generalization
References
[1] D. Patel, “Enhancing Banking Security: A Blockchain and Machine Learning- Based Fraud Prevention Model,” Int. J. Curr. Eng. Technol., vol. 13, no. 06, Dec. 2023, doi: 10.14741/ijcet/v.13.6.10.
[2] V. Kavitha, G. H. Kumar, S. V. M. Kumar, and M. Harish, “Churn Prediction of Customer in Telecom Industry using Machine Learning Algorithms,” Int. J. Eng. Res., vol. V9, no. 05, pp. 181–184, May 2020, doi: 10.17577/IJERTV9IS050022.
[3] H. Kali, “Optimizing Credit Card Fraud Transactions Identification And Classification In Banking Industry Using Machine Learning Algorithms,” Int. J. Recent Technol. Sci. Manag., vol. 9, no. 11, pp. 1–12, 2024.
[4] Y. Kim, “Convolutional neural networks for sentence classification,” in EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 2014. doi: 10.3115/v1/d14-1181.
[5] V. Prajapati, “Enhancing Threat Intelligence and Cyber Defense through Big Data Analytics : A Review Study,” J. Glob. Res. Math. Arch., vol. 12, no. 4, pp. 1–6, 2025.
[6] D. Patel, “Leveraging Blockchain and AI Framework for Enhancing Intrusion Prevention and Detection in Cybersecurity,” TIJER – Int. Res. J., vol. 10, no. 6, 2023.
[7] H. P. Kapadia, “Voice and Conversational Interfaces in Banking Web Apps,” J. Emerg. Technol. Innov. Res., vol. 8, no. 6, pp. g817–g823, 2021.
[8] B. Patel, H. Mallisetty, and K. M. Rao, “Artificial Intelligence Helper Application for Delivering Effective Presentations,” 2024.
[9] A. K, M. Jayan, and L. Jacob, “Categorizing Disaster Tweets Using Learning Based Models for Emergency Crisis Management,” in 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Mar. 2023, pp. 1133–1138. doi: 10.1109/ICACCS57279.2023.10113105.
[10] R. Patel, “Security Challenges in Industrial Communication Networks: A Survey on Ethernet/IP, Controlnet, and Devicenet,” Int. J. Recent Technol. Sci. Manag., vol. 7, no. 8, pp. 54–63, 2022.
[11] J. Mishra, B. B. Biswal, and N. Padhy, “Machine Learning for Fraud Detection in Banking Cyber security Performance Evaluation of Classifiers and Their Real-Time Scalability,” in 2025 International Conference on Emerging Systems and Intelligent Computing (ESIC), IEEE, Feb. 2025, pp. 431–436. doi: 10.1109/ESIC64052.2025.10962752.
[12] K. T. S, L. R. K. J, N. Manasa, N. V Sannu, and R. Firdaus, “Suspicious Activity Detection Using Convolution Neural Network and Visual Geometry Group-19,” vol. 11, no. 4, pp. 501–508, 2024, doi: 10.17148/IARJSET.2024.11471.
[13] V. Verma, “Deep Learning-Based Fraud Detection in Financial Transactions: A Case Study Using Real-Time Data Streams,” ESP J. Eng. Technol. Adv., vol. 3, no. 4, pp. 149–157, 2023, doi: 10.56472/25832646/JETA-V3I8P117.
[14] R. Q. Majumder, “A Review of Anomaly Identification in Finance Frauds Using Machine Learning Systems,” Int. J. Adv. Res. Sci. Commun. Technol., pp. 101–110, Apr. 2025, doi: 10.48175/IJARSCT-25619.
[15] D. D. Rao, D. Dhabliya, A. Dhore, M. Sharma, S. S. Mahat, and A. S. Shah, “Content Delivery Models for Distributed and Cooperative Media Algorithms in Mobile Networks,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, Jun. 2024, pp. 1–6. doi: 10.1109/ICCCNT61001.2024.10724905.
[16] H. P. Kapadia, “Generative AI for Real Time Conversational Agents,” Int. J. Curr. Sci., vol. 13, no. 3, pp. 201–208, 2023.
[17] M. Arafat, A. Qusef, and G. Sammour, “Detection of Wangiri Telecommunication Fraud Using Ensemble Learning,” in 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, JEEIT 2019 - Proceedings, 2019. doi: 10.1109/JEEIT.2019.8717528.
[18] M. Liu, J. Liao, J. Wang, and Q. Qi, “AGRM: Attention-Based Graph Representation Model for Telecom Fraud Detection,” in IEEE International Conference on Communications, 2019. doi: 10.1109/ICC.2019.8761665.
[19] G. Mantha, “Transforming the Insurance Industry with Salesforce: Enhancing Customer Engagement and Operational Efficiency,” North Am. J. Eng. Res., vol. 5, no. 3, 2024.
[20] M. Gridach, H. Haddad, and H. Mulki, “Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge,” in 3rd Workshop on Noisy User-Generated Text, W-NUT 2017 - Proceedings of the Workshop, 2017. doi: 10.18653/v1/w17-4403.
[21] R. Patel, “Automated Threat Detection and Risk Mitigation for ICS (Industrial Control Systems) Employing Deep Learning in Cybersecurity Defence,” Int. J. Curr. Eng. Technol., vol. 13, no. 6, 2023.
[22] V. Shah, “Scalable data center networking : Evaluating virtual extensible local area network-Ethernet virtual private network as a next-generation overlay solution,” Asian J. Comput. Sci. Eng., vol. 8, no. 3, pp. 1–7, 2023.
[23] K. Mallikarjuna Rao Bhavikkumar Patel, “Suspicious Call Detection and Mitigation Using Conversational AI,” Defensive Publ. Ser., 2023.
[24] D. Rao, “Strategizing IoT Network Layer Security Through Advanced Intrusion Detection Systems and AI-Driven Threat Analysis,” J. Intell. Syst. Internet Things, vol. 12, no. 2, pp. 195–207, 2024.
[25] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” 1st Int. Conf. Learn. Represent. ICLR 2013 - Work. Track Proc., pp. 1–12, 2013.
[26] J. Zhong and W. Li, “Predicting Customer Call Intent by Analyzing Phone Call Transcripts Based on CNN for Multi-Class Classification,” 2019. doi: 10.5121/csit.2019.90702.
[27] D. D. Rao, S. Madasu, S. R. Gunturu, C. D’britto, and J. Lopes, “Cybersecurity Threat Detection Using Machine Learning in Cloud-Based Environments: A Comprehensive Study,” Int. J. Recent Innov. Trends Comput. Commun., vol. 12, no. 1, 2024.
[28] N. K. Prajapati, “Federated Learning for Privacy-Preserving Cybersecurity: A Review on Secure Threat Detection,” Int. J. Adv. Res. Sci. Commun. Technol., pp. 520–528, Apr. 2025, doi: 10.48175/IJARSCT-25168.
[29] A. Pravin, B. L. S. Bizotto, M. Sathiyanarayanan, and T. P. Jacob, “Enhanced Framework to Predict Customer Churn Using Machine Learning,” in 2025 International Conference on Inventive Computation Technologies (ICICT), 2025, pp. 8–12. doi: 10.1109/ICICT64420.2025.11005219.
[30] J. Li, C. Zhang, and L. Jiang, “Innovative Telecom Fraud Detection: A New Dataset and an Advanced Model with RoBERTa and Dual Loss Functions,” Appl. Sci., vol. 14, no. 24, p. 11628, Dec. 2024, doi: 10.3390/app142411628.
[31] M. F. Alhakim, J. Petchhan, and S.-F. Su, “Leveraging TabNet for Enhanced Customer Churn Prediction in the Telecommunication Industry,” in 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 2024, pp. 717–718. doi: 10.1109/ICCE-Taiwan62264.2024.10674508.
[32] S. Saha, C. Saha, M. M. Haque, M. G. R. Alam, and A. Talukder, “ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry,” IEEE Access, vol. 12, pp. 4471–4484, 2024, doi: 10.1109/ACCESS.2024.3349950.
[33] I. Aattouri, H. Mouncif, and M. Rida, “Call Center Customer Sentiment Analysis Using ML and NLP,” in Proceedings - SITA 2023: 2023 14th International Conference on Intelligent Systems: Theories and Applications, 2023. doi: 10.1109/SITA60746.2023.10373715.
[34] C. Xiong, “Telecom Fraud Detection Using Machine Learning KTH Thesis Report,” Degree Proj. Comput. Sci. Eng., 2022.
[35] R. P. H. Liyanage, B. T. G. S. Kumara, B. Kuhaneswaran, and S. Prasanth, “Deep learning approach for detecting customer churn in telecommunication industry,” in Social Customer Relationship Management (Social-CRM) in the Era of Web 4.0, 2022. doi: 10.4018/978-1-7998-9553-4.ch009.
[36] H. Li, Y. Wei, and C. Zhu, “Convolutional Neural Network Analysis for Modulation Classification of Wireless Communication Signal,” Adv. Transdiscipl. Eng., vol. 30, pp. 827–833, 2022, doi: 10.3233/ATDE221103.
[37] V. Chang, K. Hall, Q. A. Xu, F. O. Amao, M. A. Ganatra, and V. Benson, “Prediction of Customer Churn Behavior in the Telecommunication Industry Using Machine Learning Models,” Algorithms, vol. 17, no. 6, 2024, doi: 10.3390/a17060231.
[38] N. M. AbdelAziz, M. Bekheet, A. Salah, N. El-Saber, and W. T. AbdelMoneim, “A Comprehensive Evaluation of Machine Learning and Deep Learning Models for Churn Prediction,” Inf., vol. 16, no. 7, pp. 1–29, 2025, doi: 10.3390/info16070537.
[39] Y. Wei, “Telco Customer Churn Prediction,” Highlights Sci. Eng. Technol., vol. 92, pp. 218–226, 2024, doi: 10.54097/84bmrd32.
[40] M. K. Mittal, “Customer Churn Analysis in Telecom Using Machine Learning Techniques,” Dublin, National College of Ireland, 2022.
