Drivers of Time to Resolution, Application of LASSO Regression and Random Forest

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 how firm and loan characteristics and macroeconomic conditions explain time to resolution of a portfolio of loans. The approach draws from the classical statistical approach of survival models and from two machine learning methodologies (LASSO regression and random forest).

Faculty Supervisor:

Valentina Galvani;Sebastian Fossati Pereira

Student:

Adam Vanderschee

Partner:

ATB Financial

Discipline:

Economics

Sector:

Finance, insurance and business

University:

University of Alberta