Dynamic Credit Scoring

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. The core idea is to establish the link between default probabilities and borrower characteristics separated in good and bad-market state scenarios. These good/bad-market-states scores are then combined using forecasts of the probability of the economy falling in either scenarios. The output is dynamic credit scoring for borrowers characteristics.

Faculty Supervisor:

Valentina Galvani;Sebastian Fossati Pereira

Student:

Junqi Wu

Partner:

ATB Financial

Discipline:

Economics

Sector:

Finance, insurance and business

University:

University of Alberta