Fraudulent accounting practices pose a significant threat to the stability and integrity of banking systems, leading to financial losses, reputational damage, and systemic risks. This study proposes a deep learning-based framework for detecting fraudulent accounting transactions using a benchmark financial dataset from the UCI repository. The methodology incorporates data preprocessing, feature extraction, and feature engineering to enhance model performance, followed by the development of advanced neural architectures including Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). Comparative evaluation reveals that LSTM outperformed other models with an accuracy of 96.4% and an AUC of 0.981, demonstrating superior capability in identifying complex sequential fraud patterns. The integration of these models into U.S. financial institutions is discussed, highlighting their potential to improve regulatory compliance, strengthen fraud risk management, and ensure greater transparency in financial reporting. This research contributes to the growing body of knowledge in financial fraud detection by showcasing the application of deep learning techniques to fraudulent accounting, providing both academic and practical implications for the banking sector.

Fraudulent accounting detection, deep learning, banking system, LSTM, UCI dataset, financial fraud, artificial intelligence, model evaluation

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