Leveraging LightGBM for High-Accuracy Telecom Fraud Detection with Clustering-Based Undersampling

This study presents a machine learning framework designed to predict and prevent telecom fraud by analyzing 1 million transaction records. The model identifies critical fraud patterns through the analysis of key features, including PIN usage, transaction frequency, and location. To address the severe class imbalance in the dataset (initially a 10:1 ratio), a clustering-based undersampling technique was employed, balancing the dataset to a 1:1 ratio while preserving data integrity and improving model performance. The framework utilizes LightGBM, optimized through Bayesian hyperparameter tuning and five-fold cross-validation, achieving an accuracy of 98% and a robust AUC of 0.9. Key findings highlight that transactions involving both bank cards and PIN verification exhibit a drastically reduced fraud risk (0.0001 ratio), while cardless or PIN-less transactions are significantly more susceptible to fraud. The study emphasizes the importance of multi-factor authentication and provides actionable insights for financial institutions to mitigate fraud risks. Additionally, it underscores the transformative potential of machine learning in real-time fraud detection, with future opportunities for integrating emerging technologies like blockchain to further enhance security.