Beyond Anomaly Detection: Redesigning Real-Time Financial Fraud Systems for Multi-Channel Transactions in Emerging Markets

Abstract

The growing adoption of mobile banking, e-wallets, USSD platforms, and crypto gateways in emerging markets has led to a surge in complex, multi-channel financial fraud. Traditional fraud detection systems, which primarily rely on static rule-based or anomaly-focused models, struggle to adapt to the evolving behavioral and transactional patterns within these environments. This study proposes a real-time, machine learning-based fraud detection framework that integrates transactional data across multiple financial channels to enhance detection accuracy and response speed. The model architecture combines channel-specific classifiers, Random Forests for structured data, LSTM networks for temporal sequences, and XGBoost for ensemble learning, into a unified meta-learning system capable of cross-channel fraud correlation. Behavioral profiling, device fingerprinting, and time-based aggregation are employed to enrich feature spaces and capture nuanced fraud signatures. Evaluation was conducted using a large-scale, multi-source dataset of financial transactions from various digital platforms in an emerging market context. Performance was assessed using AUC-ROC, precision, recall, F1-score, and detection latency. Results show that the proposed system significantly outperforms conventional single-channel anomaly detection models, achieving a 94.6% F1-score, reducing false positives by 36%, and detecting fraudulent activity within an average latency of 230 milliseconds. The findings demonstrate the feasibility and necessity of a multi-channel, behavior-aware, real-time fraud detection pipeline tailored for the unique challenges in emerging financial ecosystems.