A couple of new partnerships in the trade finance space promise to have implications for corporates. First up, Taulia has announced a strategic alliance with J.P. Morgan that aims to create a differentiated trade finance solution for clients.
The strategic initiative offers J.P. Morgan’s clients both the capability to onboard suppliers of all sizes across the globe and the flexibility to toggle seamlessly between bank-funded and self-funded early payments. Through Taulia’s platform, clients will be able to inject liquidity to strengthen their supply chain while simultaneously giving them more visibility and control over their cash. Ultimately, this should unlock trapped working capital within corporate supply chains.
“We’re always looking for impactful ways to enhance the client experience within Wholesale Payments and that’s been proven by our various investments over the last three years - from acquisitions to strategic alliances like what we’re doing with Taulia,” said Takis Georgakopoulos, global head of Wholesale Payments at J.P. Morgan. “With Taulia, we’re better positioned to serve our clients for the long term, allowing them to inject and redeploy liquidity to their suppliers, ensuring continued operations during this challenging time.”
This is J.P. Morgan’s most significant strategic alliance with a fintech in the trade finance business, as the firm looks to leverage Taulia’s industry leading technology platform, data and analytics to enhance and optimise corporate supply chains.
“Combining Taulia’s technology and delivery with J.P. Morgan’s global reach creates an unmatched value proposition for clients,” said Cedric Bru, CEO of Taulia. “Our mission is to allow businesses to thrive by having access to cash in a predictable and cost-effective manner. This strategic alliance further strengthens our purpose.”
Going quantum in Singapore
Meanwhile, in Singapore, another new partnership is looking at the application of quantum computing in the trade finance space. Specifically, Singapore Management University (SMU) and Tradeteq will be embarking on a project entitled “Exploring the Advantages of a Quantum System for Machine Learning applied to Credit Scoring.” This project aims to develop quantum computing-based credit scoring methods for companies. The project is supported by the Monetary Authority of Singapore under the Financial Sector Technology & Innovation (FSTI) - Artificial Intelligence and Data Analytics (AIDA) Grant Scheme.
SMU and Tradeteq’s objective for the project is to build a predictive machine learning model that 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 is designed to 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.
“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,” said Michael Boguslavsky, head of AI at Tradeteq. “Quantum computing is set to be a gamechanger for many sectors, and we’re excited to be leading the charge for trade finance.”
“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,” commented Professor Pang Hwee Hwa, Dean of SMU School of Information Systems. “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.”
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