Forecasting Ability of Non-consumer Scorecards and their Ability to Predict Probability of Default

International Financial Reporting Standards (IFRS) for loss allowances are changing, and financial institutions are proactively adapting existing methodologies and developing new ones to remain compliant. The main ingredient in the myriad of evaluations that banks are required to perform for compliance is risk assessment. The first goal of this research project is to review best practice risk models, with a special focus on modeling the evolution of default probabilities. In particular, the project evaluates the forecasting ability of internally credit ratings of borrowers in predicting probability of default. The linkage between internal scores and probability of default will be explored using a classical statistical approach (re-logit) and a methodology stemming from recent advances in machine learning (random forest with undersampling).

Intern: 
Stephen Tearoe
Faculty Supervisor: 
Valentina Galvani;Sebastian Fossati Pereira
Province: 
Alberta
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