Degree
Master of Science in Data Science
Department
Department of Computer Science
Faculty/ School
School of Mathematics and Computer Science (SMCS)
Date of Submission
Fall 2024
Supervisor
Jawwad Farid, Professor of Practice, Institute of Business Administration, Karachi
Keywords
Liquidity Risk Assessment, Liquidity Coverage Ratio (LCR), Net Stable Funding Ratio (NSFR), Capital Adequacy Ratio (CAR), Early Warning Systems, Financial Anomaly Detection
Abstract
This project focuses on assessing financial and liquidity risk parameters in line with the Internal Liquidity Adequacy Assessment Process (ILAAP) and developing a machine learning-based framework to detect early signs of anomalies in liquidity risk assessment. The study analyzes financial data from five major banks in Pakistan over a ten-year period to identify key risk assessment cohorts, including Liquidity Coverage Ratio (LCR), Leverage Ratio (LR), Net Stable Funding Ratio (NSFR), Capital Adequacy Ratio (CAR), Return on Assets (ROA), and Advance to Core Deposit Funding. By applying machine learning models, the project aims to provide predictive insights into potential liquidity distress, enabling early intervention and improved risk management. The proposed framework automates the detection of warning signals in liquidity management, contributing to more robust financial risk governance and compliance with regulatory requirements. This project demonstrates how data-driven approaches can enhance liquidity risk management and strengthen the financial resilience of banking institutions
Document Type
Restricted Access
Submission Type
Research Project
Recommended Citation
Imran, M. (2024). Machine Learning Based Framework for Assessing Financial Institutions’ Liquidity (Unpublished graduate research project). Institute of Business Administration, Pakistan. Retrieved from https://ir.iba.edu.pk/research-projects-msds/53
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