Photo by Mike Enerio on Unsplash
SMU and Tradeteq’s objective for the project is to build a predictive machine learning model which has the potential to improve credit scoring accuracy. The model will be implemented on both a quantum computer and a simulated quantum computer.
Quantum computers enable the processing of multiple pieces of data at the same time by using the unknown quantum state of very small particles, rather than the classical use of transistors, as the basis of computing. This may sometimes lead to far quicker processing times over standard classical machines.
The project will enable SMU and Tradeteq to develop quantum neural network algorithms and research optimal configurations of artificial neurons. This may eventually enable quicker credit assessment taking into account growing volume and variety of data that flows in Tradeteq systems.
Tradeteq currently uses AI to provide accurate and up-to-date credit scores to SMEs who would not normally be able to access financing. Through this collaboration, Tradeteq will be improving their long-term capabilities and stay at the cutting edge of global AI research for financial applications.
Faculty from SMU School of Information Systems has experience in quantum devices and the application of disruptive technologies to financial technologies. This collaboration will further the research of applying quantum computing to real problems in industry.
This project will use quantum algorithms, which cannot be implemented on today’s classical machines. SMU’s and Tradeteq’s work may be the first to show a practical quantum advantage for a financial application. The results may be the first beacon of business advantages for the financial industry as quantum computing continues to improve.
Tradeteq’s credit scoring algorithms are already being used on the Singapore’s Networked Trade Platform and this partnership furthers Tradeteq’s strength and presence in the region.
Michael Boguslavsky, Head of AI at Tradeteq comments: “Tradeteq’s AI credit scoring capabilities are already industry leading and this project we are embarking on with SMU is going to further develop our technology. We are exploring the development of quantum-based neural networks to more quickly and more accurately give credit scores to SMEs and transactions, allowing them access to trade finance which, under normal credit reporting, would not have been possible. Quantum computing is set to be a gamechanger for many sectors, and we’re excited to be leading the charge for trade finance.”
Professor Pang Hwee Hwa, Dean of SMU School of Information Systems, comments: “This grant will strengthen our research in applying cutting edge technologies and enable us to work with Tradeteq to develop the next generation of credit scoring networks. Currently, many small-and-medium-sized businesses are unable to grow their companies due to a lack of funding as they are deemed ‘too risky’ by current credit rating models. With shorter processing time, more businesses could be scored and with greater accuracy thereby creating more trusts and providing greater access to finance for companies than ever before.”
Quantum computers enable the processing of multiple pieces of data at the same time by using the unknown quantum state of very small particles, rather than the classical use of transistors, as the basis of computing. This may sometimes lead to far quicker processing times over standard classical machines.
The project will enable SMU and Tradeteq to develop quantum neural network algorithms and research optimal configurations of artificial neurons. This may eventually enable quicker credit assessment taking into account growing volume and variety of data that flows in Tradeteq systems.
Tradeteq currently uses AI to provide accurate and up-to-date credit scores to SMEs who would not normally be able to access financing. Through this collaboration, Tradeteq will be improving their long-term capabilities and stay at the cutting edge of global AI research for financial applications.
Faculty from SMU School of Information Systems has experience in quantum devices and the application of disruptive technologies to financial technologies. This collaboration will further the research of applying quantum computing to real problems in industry.
This project will use quantum algorithms, which cannot be implemented on today’s classical machines. SMU’s and Tradeteq’s work may be the first to show a practical quantum advantage for a financial application. The results may be the first beacon of business advantages for the financial industry as quantum computing continues to improve.
Tradeteq’s credit scoring algorithms are already being used on the Singapore’s Networked Trade Platform and this partnership furthers Tradeteq’s strength and presence in the region.
Michael Boguslavsky, Head of AI at Tradeteq comments: “Tradeteq’s AI credit scoring capabilities are already industry leading and this project we are embarking on with SMU is going to further develop our technology. We are exploring the development of quantum-based neural networks to more quickly and more accurately give credit scores to SMEs and transactions, allowing them access to trade finance which, under normal credit reporting, would not have been possible. Quantum computing is set to be a gamechanger for many sectors, and we’re excited to be leading the charge for trade finance.”
Professor Pang Hwee Hwa, Dean of SMU School of Information Systems, comments: “This grant will strengthen our research in applying cutting edge technologies and enable us to work with Tradeteq to develop the next generation of credit scoring networks. Currently, many small-and-medium-sized businesses are unable to grow their companies due to a lack of funding as they are deemed ‘too risky’ by current credit rating models. With shorter processing time, more businesses could be scored and with greater accuracy thereby creating more trusts and providing greater access to finance for companies than ever before.”
IonQ Achieves Industry Leading Performance on Next Generation Barium Qubits