Customer Lifetime Value Prediction Engine: Retail Lending Products

Canada’s financial services industry faces significant challenges to remain internationally competitive in the rapidly evolving web and big data environments. Scotiabank and its global competitors have as a key priority effective use of a large and growing amount of data to optimize the design and pricing of product offerings, to communicate effectively with clients, and to mitigate risk. A key challenge is to correctly assess the true lifetime value of customers across multiple business product lines and to develop analytical methods capable of maximizing that value while meeting customer needs. Efficient large-scale mathematical and statistical modelling methods are core requirements to meet this challenge. The proposed project will focus on development of such methods.

Faculty Supervisor:

Mikhail Nediak

Student:

Markiyan Kostiv

Partner:

Scotiabank

Discipline:

Business

Sector:

Finance, insurance and business

University:

Queen's University