Predicting the movement of financial resources into and out of corporate banking clients is a critical challenge for liquidity risk management and strategic treasury planning. Traditional forecasting models rely predominantly on linear statistical techniques and fail to capture the complex, dynamic interactions between firms and their banking relationships. This study introduces a novel predictive framework grounded in Neural Potential Field Networks (NPFNs), a hybrid architecture that integrates artificial neural networks with the theoretical principles of potential field theory drawn from physics and robotics. The proposed model conceptualizes corporate clients as dynamic agents operating within a financial potential field landscape, wherein fund inflows represent attraction forces and outflows represent repulsion forces acting upon liquidity reservoirs. By adapting the gradient-based mechanics of potential fields to model directional liquidity flows between enterprises, the framework captures both short-term transactional volatility and long-term structural patterns in corporate fund behavior. The model is trained and validated on a panel dataset comprising 12 large Iranian commercial banks covering the period 2018 to 2023, encompassing over 3,400 corporate client accounts. Empirical results demonstrate that the NPFN model achieves a Mean Absolute Percentage Error (MAPE) of 4.73% for inflow prediction and 5.21% for outflow prediction, outperforming LSTM, GRU, and traditional ARIMA benchmarks by statistically significant margins. Additionally, the model identifies key macroeconomic and microeconomic drivers including interbank interest rate spreads, corporate leverage ratios, supply chain interconnectedness, and central bank regulatory signals as primary determinants of potential field gradients. These findings provide actionable intelligence for bank asset-liability management (ALM) committees and offer a theoretically grounded, computationally tractable framework for next-generation corporate liquidity forecasting.