Genetic Algorithm Based Neural Network Framework for Fraudulent Transaction Detection

In the modern era of interconnected commerce, digital advancements have brought unparalleled convenience to financial transactions. Yet, this progress has also spawned a pressing challenge: the surge in fraudulent activities. From credit card fraud to complex money laundering, these deceptive practices inflict substantial financial losses and erode trust in the financial ecosystem. This paper introduces the Genetic Algorithm-based Neural Network (GANN) framework as a potent solution. GANN merges Genetic Algorithms (GAs) and Neural Networks (NNs), leveraging GA's optimization with NN's pattern recognition GAs emulate evolution to refine solutions iteratively, while NNs mimic brain neural connections to learn intricate patterns. GANN optimizes NN architecture via GAs, adapting to evolving fraud tactics swiftly. At its core, GANN uses GAs to mould NN architecture, evolving structures to capture transactional nuances. Experimentation on a comprehensive dataset shows GANN's prowess, boasting a remarkable 99.25% accuracy in detecting fraudulent transactions. It outperforms existing models, albeit with limitations like relying on one dataset and the potential for architecture optimization. GANN presents an innovative, proactive approach to fraud detection, learning and adapting to evolving fraudulent strategies. Beyond precise detection, it rebuilds trust, crucial for a robust financial ecosystem.