In the rapidly evolving landscape of cyber financial fraud, traditional detection methods are increasingly inadequate to counter sophisticated fraudulent activities. This study examines the potential of deep learning techniques, specifically focusing on neural networks and anomaly detection, to enhance cyber financial fraud detection. Neural networks, with their ability to model complex patterns and relationships in data, offer a robust framework for identifying fraudulent transactions. The study examines the application of various neural network architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which are adept at processing sequential data and identifying anomalies that signify fraudulent behavior. Anomaly detection, a critical aspect of this research, leverages unsupervised learning techniques to identify outliers in financial transactions that do not conform to established patterns. By employing autoencoders and generative adversarial networks (GANs), the study demonstrates how these models can effectively differentiate between legitimate and suspicious activities without the need for labeled datasets. This is particularly beneficial in the financial sector, where fraudulent patterns constantly evolve, and labeled data may be scarce or outdated. The integration of these deep learning techniques into existing fraud detection frameworks is explored, highlighting the benefits of real-time analysis and predictive capabilities. The study also addresses the challenges associated with implementing deep learning models, such as the need for high-quality data, computational resources, and the interpretability of model outputs. Furthermore, the research underscores the importance of continuous model training and adaptation to keep pace with emerging fraud tactics. By leveraging advanced neural network architectures and anomaly detection methods, financial institutions can significantly enhance their fraud detection capabilities, leading to reduced financial losses and increased security for customers. In conclusion, this study provides a comprehensive analysis of how deep learning techniques, particularly neural networks and anomaly detection, can transform cyber financial fraud detection. It emphasizes the need for ongoing research and development in this field to stay ahead of fraudsters and protect the integrity of financial systems. The findings suggest that deep learning not only enhances the accuracy and efficiency of fraud detection but also offers a scalable solution adaptable to the dynamic nature of cyber financial fraud
Keywords: Financial Fraud, Neural Networks, anomaly detection, cyber, deep learning technique
