Generating Synthetic Bank Transaction Sequences with GAN-based Models

Financial institutions gather vast amounts of data from our transactions. However, using this data directly can risk our privacy. A potential solution is generating “synthetic data”, which contains real data information without copying the original data. Generating this data, especially for bank transaction sequences, is challenging. During our research internship, we will use recent developments in deep generative models to design a generative model that can create high-quality bank transaction sequences. This research project opens doors for Verafin to team up with outside experts and lets them show potential clients how their product works using realistic, risk-free examples.

Faculty Supervisor:

Hamid Usefi

Student:

Partner:

NASDAQ Canada Inc

Discipline:

Computer science

Sector:

Artificial Intelligence; Finance and Insurance; Technology

University:

Memorial University of Newfoundland

Program: