Investing
Keeping pace with AI to fuel trade finance investment
Global Banking & Finance Review®
Company
By Michael Boguslavsky, Head of AI at Tradeteq
As the digitalisation of trade ripples its way across the globe, an increasing number of cross-border transactions have gone from what was previously a (industry-standard) 25-day processing period to being executed within a matter of hours. The accelerated digitalisation of the sector during and after the Covid-19 pandemic has helped to establish a stronger-by-the-day digital trade ecosystem that is now equipped for the introduction of artificial intelligence (AI) solutions.
AI platforms, when deployed for Trade Finance, not only support the reduction of burdensome and administrative processes, but they are also proving to be instrumental in gathering unparalleled insights and market information. The ultimate goal of AI’s integration into the sector is to better enable informed decision making when it comes to trade financing options.
Staying ahead of the digital technology adoption curve
AI is the logical next step in staying ahead of the digital technology adoption curve. Some companies have succeeded in doing so already and have since benefited from their integration of digital solutions, especially as more countries across the world continue to introduce and accept digital trade solutions for cross border transactions.
Trade financing, a crucial part of global trade and commerce is an area that has been burdened (much like traditional supply chain systems) by its antiquated systems that lack transparency and scalability, keeping investors tentative and held back due to the inability to accurately assess risk. However, with digital adoption rates high across the trade sector, the barriers to access are breaking down.
The benefit and reward of introducing AI is that by its nature it becomes better with time. As more information feeds into AI platforms, they continuously learn, adapt, and improve their performance.
Underwriters of trade finance are able to harness advanced AI models to analyse credit risk and project receivables repayments, and it also allows them to check documents instantly for any sanctions, ESG information and eligibility compliance. On the other side of this are investors that are now able to better analyse the risk and return of different trade finance assets.
An example of the successful incorporation of AI into trade finance has been with Tradeteq’s credit model, which utilises a wide array of data, merging behavioural patterns, company-specific information, geographical elements, and socio-economic factors. A significant aspect of our approach is acknowledging the importance of data completeness and diversity, and strategically managing any data gaps. This implies that in situations where specific financial data points are absent for a company, it is not excluded from training and test datasets.
Indeed, employing machine learning explicitly on trade finance data generates an abundance of crucial insights. It facilitates the detection of features that may not be apparent in a straightforward cross-sectional examination. This includes anomalies in repayment behaviours compared to a supplier’s other clients, or variations in trade payments in comparison to similarly positioned companies within the same industry or geographic location. Importantly, it is this application of AI to a richer and more extensive data pool that significantly increases the inclusion and credit scoring of Small and Medium-sized Enterprises (SMEs).
The global trade finance market is currently estimated to be $45 billion. Alongside this, the trade finance software market is expected to grow to around $3 billion by 2027.[1] As technology solutions continue to infiltrate and prove their worthiness to the market, opportunities arise for a wider pool of investors to tap into assets they would have previously avoided.
AI is the optimum solution to help narrow the existing trade finance gap. It is estimated that the trade finance gap will be reduced by 50% if the effective implementation of digital solutions is carried out.[2]
Tradeteq was among the founders of the Trade Finance Distribution Initiative (TFD Initiative), which now has close to 70 members, including some of the largest global financial institutions. The TFD Initiative’s goal is to promote the issuance and trading of trade finance instruments and is currently at the forefront of how best technology and AI is utilised in the sector.
At Tradeteq, we believe AI must be viewed as a helping hand to increase efficiency and build trust in the sector. The pace with which AI systems are being introduced means there are always risks. However, those within the sector must ensure that systems are ethical, that they align with regulatory requirements and that they are used in a collaborative way.
[1] https://www.theinsightpartners.com/pr/trade-finance-software-market
[2] https://c4dti.co.uk/wp-content/uploads/2022/10/Digital-roadmap-2023.pdf
Trade finance refers to the financial instruments and products that facilitate international trade and commerce, helping businesses manage the risks associated with cross-border transactions.
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems, enabling them to perform tasks such as learning, reasoning, and problem-solving.
Digitalization in finance involves the integration of digital technologies into financial services, enhancing efficiency, transparency, and accessibility for businesses and consumers.
Credit risks refer to the potential that a borrower will fail to meet their obligations in accordance with agreed terms, impacting lenders and investors.
A trade finance gap is the shortfall in financing available to businesses for trade transactions, often limiting their ability to engage in international trade.
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