Abstract
Financial Technology has revolutionized the delivery and usage of the autonomous operations and processes to improve the financial services. Banks and Financial institutions play a crucial role in economic development of country albeit slow to embrace IoT technology. Integrating Artificial Intelligence in IoT enhances IoT performance by analysing the information and extracting knowledge therefore making them more intelligent. so this paper enables the cloud based wireless communication network architecture integrated with IoT in Financial Technology (FinTech) where the data have been collected by cloud based server incorporated with secure transaction based authentication protocol (STAP) to identify the malicious users and fraudulent in bank transactions. The collected data from cloud server has been classified based on deep Q- learning (DQL) which uses training and testing by neural networks. By enhancing the authentication protocol during the transactions will improve the security of the banking networks and by classifying the cloud data it will identify whether there are any unauthenticated users proceed with the transaction without knowledge of the banking sector. The experimental analysis was carried out based on both secure transactions based authentication protocol and deep Q-learning through the parameters in terms of authentication protocol, security rate of the network, average end-to-end delay and cloud data based classification for secure bank transactions.