Advanced Quantitative Behavioral Models for Asset-Liability, Interest Rate Risk, and Liquidity Management in Deposit-Taking Financial Institutions

Cashflow uncertainty due to customer behaviors poses special challenges to a bank’s ability to accurately forecast its future cashflows, and therefore makes its funding and risk management difficult. In the proposed research, we plan to use cutting-edge machine learning techniques to study the behaviors of bank depositors and borrowers in Canada using an extensive proprietary data sample of the Partner Organization (i.e., EQ Bank). We will focus on two specific types of bank customer behaviors – non-maturing deposit withdrawal behavior and term-loan repayment behavior – that are critical in defining a bank’s liquidity risk. The research findings will contribute to our understanding of the behavior of Canadian depositors and borrowers, which is relatively less studied in the literature. A better understanding of their behaviors will enable the Partner Organization and other banks to enhance their financial products to better suit the needs of their Canadian customers.

Faculty Supervisor:

Peter Miu

Student:

Partner:

Equitable Bank

Discipline:

Business

Sector:

Finance and Insurance

University:

McMaster University

Program: