Standard security precautions in often changing networks are being circumvented by cyberattacks, endangering banks and other financial institutions. It is not always possible to prevent multi-vector attacks, zero-day attacks, and advanced persistent threats using supervised learning or handwritten rules. In this paper, a proposed adaptive network security framework is presented, and this framework is designed to be used in financial networks, such as bank transfer networks, trading networks, and online banking networks. This proposed framework is based on reinforcement learning, and this is where the main difference lies, since the proposed framework focuses on a control loop where simulated attacks are monitored and learned from to adapt to the way the network is kept secure. If the results of this proposed framework, based on the reinforcement learning algorithm, are compared to other approaches, it is quite clear that the proposed framework offers better performance in terms of threat detection and response.