<![CDATA[General Background: Financial institutions often face complex decision-making challenges due to the need to balance multiple, conflicting objectives such as profitability, cost control, and liquidity management. Specific Background: Traditional financial planning models struggle to accommodate these competing goals, especially in developing economies with resource constraints. Knowledge Gap: Existing studies have applied goal programming (GP) in limited industrial contexts, but its comprehensive use in the financial sector of emerging markets, such as Iraq, remains underexplored. Aims: This study aims to apply a goal programming model to optimize the financial planning of the International Development Bank for Investment and Finance (IDB) during 2016–2024, integrating multiple financial objectives under prioritized constraints. Results: The model, solved using WINQSB, achieved near-optimal solutions with minimal deviations between actual and target values across revenues, expenses, net profit, and fixed assets, confirming its effectiveness in managing competing goals. Novelty: This research introduces a weighted-preemptive hybrid goal programming framework that quantitatively aligns strategic financial objectives within constrained environments. Implications: The findings demonstrate the model’s potential as a practical decision-support tool for banks, enabling sustainable financial performance and efficient resource allocation in complex, multidimensional financial systems.Highlight : Shows how Goal Programming optimizes conflicting financial goals effectively. Demonstrates balanced achievement of revenue, cost, and profit targets in banking. Suggests the model’s practical use for sustainable financial decision-making. Keywords : Goal Programming, Weighted Method, Preemptive Method, Negative Deviation, Positive Deviation]]>
