MSc Thesis Proposal Announcement of Ahmed Shafeek:"Tree-Based Approaches to Predicting Borrowers’ Financial Performance "

Thursday, February 3, 2022 - 09:00 to 10:00


The School of Computer Science is pleased to present...

MSc Thesis Proposal by: Ahmed Shafeek 

Date:                  Thursday February 3, 2022 
Time:                  9:00am – 10:00am 
Passcode:          If interested in attending this event, contact the Graduate Secretary at with sufficient notice before the event to obtain                                  the  passcode.


The literature review we conducted pointed to that decision trees are the most successful approach in modeling financial performance and credit scoring. Comparative studies showed tree-based methods’ superiority over deep learning and other neural networks for tabular and categorical datasets. We will investigate applying Tree-Based Methods to find the feature importance and accurately predict financial performance. We chose CatBoost platform due to its efficiency, simplicity, and accuracy. We were given access to a proprietary US-based dataset, to mine and analyze, on the condition of namelessness. Profitability, risk, and bottom lines are all non-factors for this specific group of people. Predictions will not be used to refuse loans higher risk population, but rather to provide them with services and training that can better prepare them for success; a humanitarian approach to a financial problem. 
Keywords: Financial Performance Prediction, Credit Scoring, Delinquency Rate, Decision Trees, Tree-based Methods, CatBoost 

MSc Thesis Committee:  

Internal Reader: Dr. Ziad Kobti    
External Reader: Dr. Hoda ElMaraghy      
Advisor: Dr. Alioune Ngom 

 MSc Thesis Proposal Announcement                 Vector Institute in Artificial Intelligence, artificial intelligence approved topic logo



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