Conventional approaches to estimation and forecasting include the use of linear statistical models for instance linear regression equations, which are good but the relationships between variables are non-linear and this causes the models to be slightly off in their prediction. On the other hand, using neural networks and particularly LSTM and ANN has proved to enhance the ability to forecast important factors such as NPLs, CAR and liquidity ratios. As such, these models enable the central banks to distinguish between complex patterns in otherwise enormous datasets providing them with more accurate and efficient ways in which they pre-emptively respond to systematic risks. Thus in this paper, we discuss how neural networks can be used to enhance the efficiency of monetary policy by more accurate predictions on stability of the banking sector. The work also reveals the fact that neural networks are superior in terms of speed and baseline accuracy in comparison to the conventional models. The benefits of the former in achieving vital stability or continued solvency are apparent. The near aspirations for the study of these models should lie in a seamless combination of the above named hybrid models, for example coupling neural networks and reinforcement learning, as well as real-time policy updates based on constantly CL output. The paper will use the neural network architecture, owing to the characteristics of the economic time series data. That is why, LSTM networks were selected as the primary model since they are effective in the analysis of time-dependent data and can consider long-range dependencies in economic variables to find out meaningful results.