Automated Credit Risk Assessment Systems in Small Business Lending Decisions

Small businesses account for 89.6% of the total private labour force in Canada and, despite the vital role they play in the Canadian economy, fewer than half of small businesses will survive 10 years. One of the most commonly cited causes of small business failure is the inability to raise capital to finance its operations. This occurs, in part, because banks and lending agencies lack the tools necessary to draw valid conclusions from credit risk assessments. This application proposes two projects that will help build risk scoring system that the partner organization, Espresso Capital Inc., can use to make small business lending decisions. The goal is to recommend ways to improve the user experience and accuracy of the system so Espresso can predict if issues will arise in borrowing firms before they become a problem.

Faculty Supervisor:

Aziz Guergachi


Sohrab Mashhadizadeh


Espresso Capital Ltd





Ryerson University



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