A Hybrid Deep Learning Approach for Financial Fraud Detection in Enterprise Management Systems

The issue of financial fraud is one of the greatest concerns for organizations, especially in enterprise management systems where very large financial transactions are carried out. The conventional fraud detection methods are unable to assist in detecting the sophisticated fraud activities due to their complexity and volumes of data. In the paper, the idea to use the hybrid deep learning methodology is proposed in the form of convolutional neural networks (CNN) and long short-term memory (LSTM) combinations in financial fraud detection in enterprise management systems. The method combines the use of CNN for feature extraction with LSTM to determine the sequence of data in financial transactions. By combining the two models of deep learning, there is hope that the methodology will display a more conveyed outcome in the detection of fraud, as both aspects, feature learning and temporal sequence prediction, would be favorable. The performance of this hybrid model in the work was evaluated on a financial fraud data set, and its results were compared to the conventional machine learning models in accuracy and efficiency measures. The results show that the hybrid deep learning algorithm is highly superior to the current methods insofar as detection accuracy, false positives, and processing time are concerned. The paper will expand towards its end by commenting about the implications that the proposed model has on the improvement of fraud detection systems in enterprise management, controversial issues, and possible areas of research in the future.