Like most living organisms, the life cycle of a business can be divided into distinct, complex phases. These life stages are determined by various internal and external factors such as financial resource availability, managerial ability, and market conditions. The ability to model firm life stages would help financial institutions (FIs) such as ATB identify and meet the time-specific needs and challenges of their business clients. However, properly analyzing the wealth of data collected by FIs is difficult.

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 changes in creditworthiness for industry sectors.

The project aims to use state-of-the-art machine learning techniques to perform model validation. In particular, the intern will validate outcomes from risk assessment models for loan portfolios. The results will be employed to further the efficiency of ATB's internal stress testing models. The benefit for ATB financial will be the possibility to detect subsamples for which model fit might be poor, which will yield insights and, hopefully, improvement to stress testing.

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.

Banks use a myriad of methodologies to inform their officers on credit extension decisions. One of the most employed approach is to summarize borrower creditworthiness by credit scores, which in turn depend on loan default probabilities. The probability of default depends both on borrower characteristic and on the overall state of the economy. The goal of this project is to create credit scores that are responsive to the expected state of the economy.

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.

The intern will research new modelling technology to determine if the new models can make a significant improvement in servicing customers for loan approvals, debt collections, and open banking. The intern will work closely with the partner to understand the banking process and opportunity. The partner organization will receive several benefits from working with the innovative and knowledgeable intern including cross-training of techniques through collaboration, enhanced model accuracy, and enabling the partner to test new techniques.