Cyberfraud Detection on Social Media Analytics Using Machine Learning Ensemble Algorithms in e-Banking
Year: 2026
Type: article
Abstract: Social media has become a fertile ground for cyberfraud targeting e-banking users. This paper presents a step-by-step methodology for detecting cyberfraud in social media using machine learning algorithms tailored for e-banking contexts. We describe dataset construction (synthetic and real-world sources), feature engineering (textual, network, temporal, and account features), model selection (logistic regression, random forest, XGBoost, and transformer-based models), evaluation metrics (precision, recall, F1, AUC), and explainability techniques (SHAP, LIME). Experimental results on a 1,000-record sample dataset are reported with graphical representations of exploratory data analysis and model performance. The proposed pipeline demonstrates that ensemble tree models and transformer fine-tuning achieve robust detection with explainable decision drivers. Practical considerations, limitations, and suggestions for deployment in e-banking fraud operations are discussed. Keywords: Cyberfraud, Social media, e-banking, Machine Learning, Phishing, Fraud Detection
Institution: Osmania University
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7165001972
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