High Rated Financial Transactions using Deep Learning Algorithm: A Way to Smart Banking Automation

The rapid advancements in the field of artificial intelligence and deep learning have introduced tremendous shifts in the banking industry, particularly the process of automatizing the most valued financial processes. Human involvement, processing time, and the increasingly complex nature of fraudulent activity often make traditional banking systems unable to verify and effectively process large high-value transactions. To enhance the speed of transactions, precision, and security, this research proposes a smart banking automation system that integrates deep learning algorithms with the existing financial systems. Conventional, recurrent and artificial neural networks are used in the model to analyze large volumes of transaction data, discover latent trends, and discover instances of fraud with fewer false alarms. Experiments show that deep learning-based approaches are superior to traditional machine learning approaches in terms of accuracy, fraud detection, and real-time data processing. The system will also be equipped to fulfill security in high value transactions through voice and facial recognition which is a biometric authentication. According to the results, the proposed system will offer a secure system to the current automation of banking and shorten the processing lag and enhance scalability. This paper is important to the current understanding of smart banking because it offers a deep learning-based method of managing high-valued financial transactions that is scalable, reliable, and safe.

Hali tarjima qilinmagan