Going Beyond Thin Credit, the use of Account Data

The business partner is interested in finding ways to further automate small business lending and annual renewals. In recent years, with the improvement in efficient computing and data storage and movement, the use of deposit data in lending has become more prevalent in the industry. Within industry risk managers, it is widely accepted that deposit account information has a strong predictive ability for predicting borrower risk level. However, there are no widespread industry tools similar to credit scores making use of deposit data. This project aims to employ machines techniques to develop credit scores based on deposit lending data.

Faculty Supervisor:

Valentina Galvani

Student:

Partner:

ATB Financial

Discipline:

Sociology

Sector:

Finance and Insurance

University:

University of Alberta

Program: