Analysing the Bankers’ Ratings Worldwide Using Machine Learning Techniques

Indranarain Ramlall and Moses Acquaah

International Journal of the Economics of Business, 2025, vol. 32, issue 3, 375-392

Abstract: This study applies machine learning techniques to analyse The Banker’s Top 1000 bank rankings, identifying financial metrics associated with ranking positions. The model achieves 94.16% prediction accuracy in out-of-sample tests, with decision tree models performing better than featureless baselines in regression and classification tasks. Through feature selection methods, we identify six key metrics linked to ranking performance: total assets, total liabilities, gross total loans, gross total deposits, total operating income, and loan-to-asset ratio. These metrics provide insights into ranking outcomes. This work is the first to apply neural networks and feature selection to a decade-long dataset of The Banker’s rankings, offering empirical insights for evaluating global financial performance.

Date: 2025
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DOI: 10.1080/13571516.2025.2531821

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