Financial Sustainability and Performance Evaluation of American Financial Institutions Using Data Analytics and Machine Learning
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Abstract: This dissertation aims to quantify, evaluate, and predict financial sustainability in the banking industry. Extant studies that access financial sustainability primarily focus on microfinance institutions (MFIs), which provide services to the financially marginalized population. This dissertation investigates financial sustainability in the commercial banking sector at the firm level. Data analytical methods are utilized to predefine and benchmark financial sustainability as an organizational performance. Specifically, data envelopment analysis (DEA) is employed due to its embodiment of an input-to-output structure inherent in all production processes. Performance metrics, including resources consumed and outputs produced, are selected manually based on empirical studies to build this composite index. A simple, one-stage composite index is first built, and then a more complex multi-stage network structure is developed. Then, different machine learning methods are explored, and several appropriate methods are utilized to construct methodological frameworks that predict financial sustainability in the banks’ deposit related operations, loan related operations, as well as profitability conversion. The methods adopted include classification and feature selection using random forests, support vector machines (SVM) and logistic regression, synthetic minority oversampling technique (SMOTE), and Shapley Additive Explanations (SHAP). These methods also facilitate the interpretation and extrapolation of financial sustainability evaluation results in hopes of providing meaningful insights into the banking industry. Dissertation Committee:
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April 13, 2023 (Thursday) Zoom link 
Yu Shi Yu Shi is a PhD candidate at the WPI Business School. Her research interests are in the fields of benchmarking, performance evaluation and data analytics. Yu’s current research primarily focuses on using data analytics and machine learning methods for the performance evaluation of the American banking industry. Yu has published in Annals of Operations Research, European Journal of Operational Research, and Journal of the Operational Research Society. Yu has presented her works at operations research and decision sciences conferences, including INFORMS and DSI. Prior to starting her PhD program at WPI, Yu obtained a master’s degree in finance from Queen’s University (Canada) and a bachelor’s degree in finance and economics from University of Toronto. Contact: yshi2@wpi.edu |
