Anomaly Detection in Financial Transactions using Hybrid Data Mining Approaches

Abstract

The exponential growth of digital financial services has led to a surge in fraudulent activities, necessitating robust anomaly detection mechanisms to safeguard financial transactions. Traditional rule-based systems often fall short in detecting sophisticated and evolving patterns of financial fraud. This study investigates a hybrid data mining approach for detecting anomalies in financial transactions, combining multiple algorithms to enhance detection accuracy and reduce false positives. The hybrid approach integrates supervised learning methods such as decision trees and support vector machines (SVM) with unsupervised techniques like k-means clustering and autoencoders to model normal and abnormal behaviors effectively.

This research utilizes a real-world financial transactions dataset to evaluate the performance of the hybrid model. Feature selection techniques are applied to enhance model efficiency, and the dataset is preprocessed to handle class imbalance using Synthetic Minority Oversampling Technique (SMOTE). The experimental results indicate that the hybrid approach outperforms single-model methods in terms of precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Furthermore, this model demonstrates high adaptability in detecting previously unseen fraudulent patterns, thereby reducing financial risk.

By leveraging the strengths of both supervised and unsupervised methods, the hybrid framework offers a more comprehensive understanding of transaction behaviors, allowing for real-time monitoring and alerts. This paper contributes to the growing body of knowledge in financial fraud detection and proposes a scalable solution for financial institutions facing the dual challenge of fraud prevention and customer satisfaction.