In this work, a research of RL as an option for asset allocation algorithm algorithmic trading to enhance the decision-making power of banking institution is conducted. Four machine learning algorithms, namely Q-learning, Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Actor-Critic, the proficiency of which is investigated to improve portfolio management strategies. The models were tested on real historical market data and compared on the most important performance metrics such as ROI, risk-adjusted return, and volatility. The result was that RL-based models performed vastly better than current asset allocation models by a very significant margin, with a mean 12-month ROI of 18.5% compared to 12.3% for the traditional method. PPO performed best, at a 2.5 risk-adjusted return, followed by DQN at 1.8 and Q-learning at 1.4. Risk management was also better represented through the volatility of RL-based methods. The paper is the capability of reinforcement learning to develop dynamic, data-driven asset allocation policies that can respond more to the evolution of the market than traditional methods. The findings show that RL models, particularly PPO, can be an effective tool for banking institutions to streamline algorithmic trading operations.