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Faculty of Graduate Studies

Muhammad Qasim Idrees

Notice of the Final Oral Examination for the Degree of Master of Applied Science

Topic

Predicting Bankruptcy Using Machine Learning

Department of Electrical and Computer Engineering

Date & location

  • Friday, August 22, 2025
  • 1:00 P.M.
  • Virtual Defence

Examining Committee

Supervisory Committee

  • Dr. Aaron Gulliver, Department of Electrical and Computer Engineering, University of Victoria (Supervisor)
  • Dr. Mihai Sima, Department of Electrical and Computer Engineering, UVic (Member)

External Examiner

  • Dr. Phalguni Mukhopadhyaya, Department of Civil Engineering, UVic

Chair of Oral Examination

  • Dr. Craig Brown, School of Medical Sciences, UVic

Abstract

Bankruptcy is a serious issue in the financial sector. ML-based approaches have been widely used to predict bankruptcy, but selecting the right classifier and identifying the key features influencing Model results is essential for achieving good performance. This is especially important in the corporate sector where decision-makers may have a limited understanding of these models and their function. There is also a need for transparency and trust in their outcomes. This thesis utilizes twelve classifiers: CatBoost, RF, SVM, KNN, NB, LR, BDT, Stacking Ensemble, AdaBoost with RF, NN, FT-Transformer, and TabNet. These classifiers are evaluated in various settings using the Polish companies dataset to predict bankruptcy. The results obtained show that CatBoost is the best performing classifier. Further, SP-LIME is used to identify features that impact model decisions. SP-LIME ranks features based on their importance. A comparative analysis of SP-LIME and PCA indicates that SP-LIME identifies more influential features.

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