Predictive Fraud Detection in Digital Payments Using Ensemble Learning
Keywords:
predictive fraud detection, ensemble learning, XGBoost, LightGBM, Random Forests, real-time analyticsAbstract
The exponential growth of digital payment infrastructures has increased fraud incidence and complexity, necessitating robust, adaptive, and predictive fraud detection. Ensemble learning algorithms like XGBoost, LightGBM, and Random Forests detect large digital payment network fraud. Ensemble models are tested for accuracy, recall, precision, and false positive mitigation using transaction-level data streams. Experimental real-time payment model generalizability and responsiveness enhance with temporal, behavioral, and transactional feature engineering. Computationally efficient boosting-based ensemble designs detect suspicious transactions better than classifiers. The paper examines high-frequency digital wallet and trading system ensemble model latency and scalability. Future financial technology ecosystem fraud prevention may use ensemble-based predictive analytics.
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References
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