Singapore’s central bank, the Monetary Authority of Singapore (MAS), has provided funding to London-based intelligent trade finance distribution platform, Tradeteq, according to the company’s news release on Monday (27 April).
This funding will allow Tradeteq to develop quantum computing-based credit scoring methods for companies.
The support from MAS came under the Financial Sector Technology & Innovation (FSTI) – Artificial Intelligence and Data Analytics (AIDA) Grant Scheme. The funded project is an exploratory research conducted jointly with Singapore Management University (SMU).
The aim of the joint effort between SMU and Tradeteq is to construct a predictive machine learning model that has the potential to boost the accuracy of credit scoring. They will utilise two types of computer to implement the model, which are quantum computer and simulated quantum computer.
As of now, up-to-date credit scores are provided to SMEs with the use of AI. These SMEs usually would not be able to access financing. By utilising quantum neural network algorithms, the research will allow for quicker credit assessment which also takes into account the increasing variety and volume of data that enters into Tradeteq’s systems.
Explaining the project, the Head of AI at Tradeteq, Michael Boguslavsky said, “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.”
He continued, “Quantum computing is set to be a gamechanger for many sectors, and we’re excited to be leading the charge for trade finance.”
On a similar note, the Dean of SMU School of Information Systems, Professor Pang Hwee Hwa remarked, “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,” he added.