The Potential of Quantum Computing in Trade Finance

Published: January 29, 2021

The financial mechanisms underpinning global trade are undergoing significant change. Tradeteq CEO Christoph Gugelmann explains how quantum computing has the potential to transform this trillion-dollar industry over the next few decades.

It is noteworthy that trade finance – an industry that has been in existence for centuries and which is set to grow to $3tr. in the next decade – remains largely paper based but is undergoing a significant wave of digitisation. Another related industry, supply chain finance, is also benefitting from this wave of progress.

The timing is not coincidental, says Gugelmann. “These advances are largely driven by fintechs that have emerged in recent years,” he explains. “Crucially, these businesses have recognised that success lies in cooperation with banks and other long-standing industry participants. Together, they are reshaping the trade ecosystem as we know it.”

One advantage already seen is a greater desire from non-bank institutions to engage in the trade finance process. This offers an opportunity to institutions seeking stable, long-term yields due to its low-risk profile and the fact that it is based on the tangible flow of goods and services.

As the large banks continue to originate and distribute to investors and other industry participants, it frees them to continue lending to small and medium sized enterprises (SMEs). “Such seamless engagement would have been difficult a decade ago, but technology has made the difference and the benefits are now being seen,” comments Gugelmann.

Qubits vs bits

Innovation and disruption occur quickly and can often take people by surprise. So, it is prudent to plan and monitor the technologies that will shape trade finance in the years and decades to come. Quantum computing is such an area worthy of exploration.  

“Quantum computers use the quantum state of very small quantum systems, or qubits, rather than classical bits, as the basis of computing,” explains Gugelmann. The idea is to rapidly exhaust all that can be predicted about a system’s behaviour as it evolves over time. The process is based on the applied mathematics of computations (rather than physics), with quantum states being used to develop algorithms for computations.

Many quantum algorithms have already been developed, while the hardware is just passing the proof-of-concept stage, he notes. “It seems only a matter of time before algorithms can be paired with a large enough logical qubit infrastructure to achieve quantum supremacy.”

The technology promises to become a game-changer in many fields, including trade finance, he believes. “Depending on the use case, quantum computing can lead to a polynomial or even exponential speed up of the processing time over standard classical machines.”

One promising area for quantum computing is machine learning and deep learning, two facets of artificial intelligence (AI) that are attracting much attention. “At Tradeteq, we are exploring the extent to which simulated quantum or actual quantum computing can help with classification and prediction problems, including credit scoring,” Gugelmann reports. “We are also experimenting with simulated quantum algorithms for portfolio optimisations, where the early results have been quite promising.”

Quantum transaction scoring

Broadly speaking, there are two forms of credit scoring that are commonly used: company credit scoring and transaction credit scoring. Trade finance requires a scoring that captures all relevant transactional risk elements including credit risk, dilution risk and fraud risk.

For a trade finance transaction there is a large amount of data to process, from real-time ship-tracking, to data on similar transactions. Transaction data can involve the buyer or seller, their peer group, transactions of similar goods or between similar countries. Unlocking previously unavailable vast data flows will challenge many existing models.

“The current generation of transaction models can handle tens of thousands of variable updates daily. But we need to prepare the next generation of models that can increase such capabilities intelligently,” warns Gugelmann. “This is where future quantum machine learning can analyse quantum states and systems.”

Tradeteq has embarked on a research project with Singapore Management University (SMU), which has significant experience in quantum devices and the application of disruptive financial technologies. The project is given such significance as to be supported by the Monetary Authority of Singapore (MAS) under its Financial Sector Technology & Innovation (FSTI) – Artificial Intelligence and Data Analytics (AIDA) Grant Scheme.

Initially, the team will be looking at replicating simple ‘toy-company’ level models on quantum systems to learn how they can be used for more complex models, when the capacity of those systems improves enough to challenge the current state-of-the-art classical implementations.

Future-gazing

Getting to a point where quantum computing can match the functionality of classical systems will take many years, so this is not something that is expected to be in a production system any time soon.

But fintech innovation in trade finance is gathering pace. “As we have seen in other industries, it is vital to look ahead, have long-term goals in mind and invest in technology and innovation for the long term, or risk becoming obsolete,” comments Gugelmann. “In an industry like trade finance, which has seen little change over centuries, there is clear potential for transaction scoring data to be reformulated in such a way that surely they will be able to be solved by quantum computing.”

The successful application of this technology could indeed be a game-changer. Not only will it enhance the technological and operational capabilities of institutions but it could become the key to achieve greater transparency, increased participation from SMEs globally, and at last a reduction in the $1.5tr. trade finance gap.