A Deep Learning Based Hybrid Model Using LSTM and CNN Techniques for Automated Internal Fraud Detection in Banking Systems

The present work investigates the application of deep learning for detecting fraud in banks, particularly insider fraud that poses high risks to the financial sector. conventional rule-based systems are inadequate in the detection of various complexities brought by fraudsters with internal access to the companies’ accounts. To this end, an anomaly detection system with LSTM and CNN for transactional pattern evaluation was designed. Raw financial data and strictly generated fraud cases were used; the data were preprocessed with Pandas and NumPy; models’ training and their evaluation were carried out with TensorFlow and PyTorch. The proposed model also has 98.4% accuracy of detecting fraudulent transactions with 1.7% false positive rate. It was further confirmed that using the new approach has an improvement of 12% in the fraud detection compared with other traditional methods of machine learning. The evidences reported that insider fraud can be detected through the use of deep learning techniques while with a little needed for human involvement. This study emphasizes the importance of applying artificial intelligence in the security of banking systems and future developments of the explainable artificial intelligence in combating fraud in the sector.