Abstract
Liquidity risk management (LRM) has recently grown into a considerably more important role in promoting the operational and financial soundness of banking and financial institutions in the wake of the 2008 global financial crisis and the following economic upheavals caused by the COVID-19 pandemic. The risk that an entity may not be in a position on to settle the financial obligations as they fall due, without loss that may be unacceptable or impacting its daily undertakings, is known as liquidity risk as far as the Baselessly Committee on Banking Supervision is concerned. In essence, it deals with the capacity of financial institutions to transform bank assets into to a form of readily available cash, especially in stressed market conditions. In a situation that was not well checked, liquidity risk may trigger dire effects, such as bank runs, failure of financial intermediaries,, and general systemic shocks, as witnessed during the bust of Lehman Brothers in 2008 (Shem and Mupa, 2024). The event confirmed the weakness of the classical liquidity management tools and models, which used to be based on stubborn indicators and the past without appropriate attention to the current market situation and potential future stressful scenarios.
Citations
IRE Journals:
Carlos Postigo Toledo , Judith Saungweme , Neliswa Clementine Matsebula , Mabasa Masunungure , Munashe Naphtali Mupa
"Leveraging Big Data and AI for Liquidity Risk Management in Financial Services" Iconic Research And Engineering Journals Volume 9 Issue 2 2025 Page 522-530
IEEE:
Carlos Postigo Toledo , Judith Saungweme , Neliswa Clementine Matsebula , Mabasa Masunungure , Munashe Naphtali Mupa
"Leveraging Big Data and AI for Liquidity Risk Management in Financial Services" Iconic Research And Engineering Journals, 9(2)
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