Rea Press null Rea Press3009-44613009-4461 Rea Press https://doi.org/10.22105/tqfb.v2i1.49 Research Article Asset liability management Machine learningPerformance evaluationProfitability Performance of Banks’Asset Liability Management Strategies: A Practical Approach with Machine LearningPerformance of Banks’Asset Liability Management Strategies: A Practical Approach with Machine Learning Banihashemi Banihashemi Department of Mathematics, Allameh Tabataba'i University, Tehran, Iran. Kazemi Reza Department of Mathematics, Allameh Tabataba'i University, Tehran, Iran. 05 2025 01 05 2025 2 1 © 2025 Rea Press 2025

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 Performance of Banks’Asset Liability Management Strategies: A Practical Approach with Machine Learning

This research examines the performance of banks' Asset Liability Management (ALM) strategies using Data Envelopment Analysis (DEA) to improve bank efficiency and estimate the efficiency scores of emerging banks. ALM is an essential process for financial institutions to manage their assets and obligations effectively, ensuring profitability, liquidity, and risk oversight, while DEA offers a comprehensive methodology for evaluating and comparing the efficiency of Decision-Making Units (DMUs). By utilizing DEA in the context of ALM, this research seeks to uncover inefficiencies and recommend optimization strategies. The results reveal considerable differences in efficiency levels, underscoring potential improvement areas and best practices. This study adds to the existing literature by illustrating the practical use of DEA in ALM and providing actionable insights for banks to boost their performance.

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