Building Trust in Digital Transactions: An Ensemble Machine Learning Approach for Fraud Detection

As digital transactions grow in number and complexity, the sophistication of fraudulent schemes is rising in parallel. Current fraud detection methods, relying on static rules or single algorithms, are increasingly insufficient against these dynamic threats. This research proposes a novel ensemble-based approach to fraud detection, incorporating advanced machine learning techniques to enhance accuracy and adaptability. By combining models such as deep learning, anomaly detection, and conventional algorithms, our approach captures the nuances of fraudulent behavior even in the absence of massive datasets. Additionally, strategies are developed to address the challenges posed by unbalanced datasets, common in financial transaction data. Our findings demonstrate that this ensemble approach outperforms individual models, reducing false positives and increasing detection accuracy, thereby fostering trust and security in digital environments.