This study examines how intermediation services provided by banks—proxied by loans-to-assets and deposits-to-assets ratios and supported by controls such as net interest margin, cost-to-income, and size—relate to efficiency indicators measured by return on assets (ROA) and return on equity (ROE). To demonstrate the empirical workflow in the absence of shared proprietary data, we construct an illustrative cross-sectional dataset that reproduces realistic ranges observed in bank balance sheets and income statements. The analysis articulates a replicable pipeline: construct variables, motivate functional form, estimate models, and translate diagnostics into managerial and policy meaning. Results suggest that stronger credit and deposit intermediation are positively associated with ROA and ROE after accounting for margin, cost efficiency, and size, but the strength of association depends on cost discipline and margin conditions. The discussion emphasizes how variable selection, item construction, and diagnostic checks shape interpretability, and it cautions against naïve causal inference without panel designs, instruments, or exogenous shocks. The article concludes with practical recommendations for researchers and bank analysts who seek to align intermediation strategy with performance dashboards and stresses transparency through model documentation and robustness routines.

Bank intermediation, ROA, ROE, efficiency indicators, regression analysis

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