Abstract
Financial fraud continues to pose significant challenges to the banking and finance industries, with evolving tactics and increasingly sophisticated techniques being used by fraudsters. Neural networks, a class of machine learning models, have shown great promise in detecting and preventing financial fraud by analyzing large datasets and identifying unusual patterns in financial transactions. This article explores the application of neural networks in financial fraud detection, discussing their ability to improve the accuracy and efficiency of fraud detection systems. It also examines the challenges and opportunities associated with integrating neural networks into fraud detection systems and their potential to enhance security in financial transactions.
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