Fraud detection in the banking sector using Gated Green Anaconda Progressive Generative Axial Adversarial Attention Network

In the realm of digital banking, financial fraud has become an escalating concern due to the rapid adoption of online transaction systems. In the banking sector, fraud detection is critical, when deceptive practices outcome in large financial losses and can undermine trust in banking institutions. Traditional detection methods often struggle with false positives, high computational costs, and adaptability to evolving fraud patterns. This work is recommended to solve this problem. This work deals with effective identification of the various types of fraud that exist in the banking industry using a complex network known as the Gated Green Anaconda Progressive Generative Axial Adversarial Attention Network (2GAP-G3A-Net). The datasets used in this work include Financial-Fraud-Detection, and synthetic data that are derived from the financial payment system dataset that needs to be preprocessed for cleansing and normalization uses NLP based methods. Feature selection is carried out using the sea horse optimization algorithm to reduce model complexity and improve effectiveness of the model in predicting fraud cases while selecting the most important variables. The 2GAP-G3A-Net is then used to develop another state of the art and highly accurate fraud detection framework that integrates the PgAN and GAO with an additional GAAN to identify intricate transactional patterns. These techniques for using the 2GAP-G3A-Net model demonstrate a nearly perfect mean per-voxel Dice coefficient of 0.999 and show the efficiency of the model and higher potentiality compared with the current techniques. The approach enhances the accuracy of fraud prediction and minimizes false positives, can operate in a constantly changing environment and work with large data sets, which make it an effective tool in the banking industry.