The Role of AI in Financial Fraud Detection: A Deep Learning Approach
Abstract
With the increasing sophistication of financial fraud schemes, AI-driven solutions have become essential in detecting fraudulent activities. This study proposes a deep learning-based fraud detection system leveraging anomaly detection techniques, recurrent neural networks (RNNs), and ensemble learning methods. We train and evaluate our model on real-world financial transaction datasets, demonstrating its ability to detect fraudulent patterns with high precision and recall. The results indicate that AI-powered fraud detection significantly enhances security in financial systems, reducing false positives and improving risk management in banking and fintech sectors.
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