Improving Safety and Efficiency of Payment Systems using AI and Quantum Computing

Speaker:  Ajit Desai | Bank of Canada

Date: May 27, 2024
Time: 4 pm
Room: LH3058 (Lazaridis Hall (Math Boardroom), Room 3058) & Hybrid

Abstract: High-value payment systems (HVPSs), like Lynx in Canada and Fedwire in the US, are the backbone of a nation's financial infrastructure. These real-time gross settlement (RTGS) platforms handle thousands of high-value transactions between financial institutions daily, making their safety and
efficiency paramount to financial stability and economic growth. To enhance safety, we propose a flexible machine learning framework for real time transaction monitoring in HVPSs. This framework assists system operators and overseers in detecting anomalous transactions that, if caused by a cyberattack or operational outage and left undetected, could have serious implications for HVPSs, their participants, and the broader financial system. To improve efficiency, we introduce payment reordering. Finding the optimal transaction ordering is a combinatorial optimization problem; Therefore, we developed a quantum algorithm and implemented it on a hybrid quantum annealing solver to identify an optimal payment ordering that minimizes the amount of liquidity required without significantly increasing payment delays. By combining these cutting-edge techniques, we propose a comprehensive solution that enhances both the safety and efficiency of payment systems, paving the way for more resilient financial infrastructures

Bio: Ajit Desai is currently working as a principal data scientist in the research division of Bank of Canada's Banking and Payments Department. His work leverages cutting edge techniques such as artificial intelligence, machine learning, and quantum computing to study payments data (including
cryptocurrency data) with the primary objective of making Canada’s digital payments infrastructure safe and efficient. Dr. Desai received his Ph.D. from Carleton University in 2018 in Computational Science and Engineering and M.S. from Indian Institute of Technology Madras