Anomaly Detection in Financial Datasets Using Autoencoders

Fraud detection is a critical issue in various industries, including finance, healthcare, and e-commerce, where the detection of fraudulent activities can prevent significant financial losses and protect sensitive information. Traditional fraud detection methods often rely on rule-based systems or statistical models, which may struggle to adapt to evolving fraud patterns and may not effectively capture complex fraud schemes. In recent years, there has been growing interest in leveraging advanced machine learning techniques, such as Autoencoders, to improve fraud detection accuracy and efficiency. In this paper, we explore the application of autoencoders for anomaly detection in financial datasets, specifically targeting fraud detection in credit card and insurance data. Due to the scarcity of labelled fraudulent transactions, we propose an unsupervised learning approach using autoencoders to identify anomalies. We compare the performance of autoencoders with traditional machine learning techniques, including Support Vector Machines (SVM) and Logistic Regression. Our experiments demonstrate that autoencoders are effective in handling imbalanced datasets and detecting fraudulent activities with high precision.