AI-powered fraud detection: A comparative analysis of deep learning models in financial systems

Modern banking and digital transaction security require artificial intelligence solutions because financial fraud represents a major contemporary threat. Real-world financial fraud detection becomes challenging when traditional automated approaches such as rule-based and statistical models must handle modern large-scale fraudulent activities. This research analyzes deep learning model implementations for fraud detection by examining Convolutional Neural Networks (CNNs) and recurrent neural networks (RNNs), along with Long Short-Term Memory (LSTM) networks, Autoencoders, and mixed solution methods. This research establishes results based on the performance evaluation of accuracy, precision, recall, and F1-score to determine the models' effectiveness. The research evaluates actual instances of financial fraud throughout the analysis to demonstrate AI model deployment in real-world practice. The study executes a systematic research framework that combines information collection with data preparation followed by model testing to demonstrate appropriate findings alongside recommendations for fraud detection enhancement. The research illustrates how enhanced financial security becomes achievable through AI-based fraud detection improvements.