Mining Financial Data for Fraud Detection using Ensemble Learning and Outlier Detection

Financial fraud poses a significant and growing threat to global economies, leading to substantial monetary losses, erosion of trust, and reputation damage for financial institutions. The increasing volume and complexity of financial trans actions necessitate robust and adaptive detection mechanisms. Traditional fraud detection methods often struggle with the inherent characteristics of financial fraud data, such as extreme class imbalance, high dimensionality, and the constantly evolving nature of fraudulent schemes. These challenges can lead to high false positive rates or, more critically, missed fraudulent activities. This paper proposes a comprehensive framework for enhancing fraud detection in financial datasets by synergistically integrating advanced ensemble learning techniques with outlier detection methodologies. Ensemble methods, such as Random Forest, Gradient Boosting Machines, are employed to combine the predictive power of multiple base learners. This approach aims to improve overall accuracy, reduce overfitting, and enhance the model’s generalization capability across diverse financial transaction patterns. Concurrently, outlier detection algorithms, including Isolation Forest or Local Outlier Factor (LOF), are utilized to identify anomalous transactions or behavioral patterns that deviate significantly from established normal financial activities.