A hybrid second-order cone programming and multi-criteria decision-making method for cost-benefit analysis in global banking

This research introduces a novel hybrid approach that combines second-order cone programming (SOCP) and multi-criteria decision-making (MCDM) to conduct a cost-benefit analysis (CBA) of global banks. Benefits correspond to the positive criteria in the banking industry, such as loans, securities, and different sources of income. On the other hand, fixed assets, deposits, personnel and interest expenses are the costs to be minimized. The approach makes it possible to measure banking performance precisely by managing massive datasets effectively and accomplishing better decision-making with complex constraints. The empirical evidence revealed that endogenous variables (fluctuations in the cost of capital, labor, loans, and securities) have a significant effect on CBA scores, while exogenous variables such as foreign direct investment abroad (FDI), economic growth (GDP growth), and political stability have minimal effects. This study provides theoretical and practical implications by demonstrating that cost management, particularly regarding operational inputs such as labor and capital expenses, is critical for enhancing bank performance. Regulatory quality, rather than external macroeconomic conditions, emerged as a dominant influence, with China offering a benchmark for effective practices. These insights can guide the refinement of cost-benefit evaluation models and inform the design of policies aimed at strengthening international banking resilience.