For years, observers of fintech have said that trade finance and its securitisation is a field ripe for the introduction of new technologies. Almost five years ago, for example, a Seeking Alpha column bore the headline “Trade finance meets disruptive technology”. 

But many participants in the industry say that the fintech disruption still has not happened, that technologies such as machine learning, blockchain and application programming interfaces have gone unexploited largely due to the fragmented nature of the market. 

Rene Canezin, managing partner and co-founder of Boston-based fund manager Evolution Credit Partners, says: “In regard to new technology coming into the space, the market players are fragmented and often smaller. The barriers to entry are high and there are, and will continue to be, significant challenges to implement new technologies.” 

Even when such innovation is incorporated into operations it creates digital islands, limited to specific use cases, so progress remains limited, and the “disruption” has not happened. 

McKinsey, in a white paper published last autumn, suggested that what is necessary to kick-start a proper disruption is an interoperability layer for trade finance tech. The white paper defines the desired layer as a “series of global shared utilities and standards” that would serve as an oceanic umbrella covering all the digital islands.  

Background considerations

What exactly is this market, which has continued on its undisrupted path in recent years? 

To get a grasp of the nature of the underlying assets, consider a small Indian concern (RiceCo) that is sending its product to Retail Chain Inc (RCI) in the US. RCI may not be a risky counterparty, but at best RiceCo may have to wait months for RCI’s payment, even after the rice has arrived in the US. This can generate cashflow issues for SME exporters. 

A trade finance fund might offer RiceCo 80 percent of the invoice value as an advance. It can get this to the exporter within 24 hours of the submission of the invoice, and contingent on confirmation that the goods have been shipped. The fund then steps into the exporter’s shoes and becomes the recipient of the money from RCI. In the event that all goes smoothly, it can soon forward the remaining 20 percent of the invoice, minus its cut, to the rice exporter. 

This way the exporter avoids the cashflow problems, and hands over to the fund any counterparty risk. 

The cut for the fund (or “factor”) can be between 1 and 4 percent. Separately, the factor charges interest on the cash advance. 

For those who invest in such funds, there are high-risk, high-yield opportunities on the one hand and safer, investment-grade plays on the other. Consider Allianz Global Investors, which launched in November 2022 its second trade finance fund, Allianz Working Capital Investment Grade Fund. Its first fund in this line, ALWOCA, (High Yield) is three years old.  

David Newman, head of global high yield at AllianzGI, says the high-yield fund “has met its return targets, delivering positive performance in 2022, and the AUM has grown to $500 million”. 

“The investor base has changed somewhat – it started with chiefly family offices but has become more institutional and more varied in terms of investor profiles and ways it fits in a strategic asset allocation.”

Asked to compare the investment grade and high-yield funds, Newman says: “The new investment grade fund offers weekly liquidity and invests only in investment-grade trade finance assets, in contrast with the high yield version of ALWOCA, which targets a higher spread and offers monthly liquidity.” 

As to the source of the risks, Canezin of Evolution Credit Partners says: “In the US, the greater threat is counterparty insolvency. Invoice fraud occurs in all markets, but the US marketplace possesses a good deal of transparency and established mechanisms for investigation and remediation.” 

Still in need of a shake-up 

Four years ago, subsequent to the Seeking Alpha column and shortly before covid made its mark, The Economist published a piece about how the trade finance system is “parochial and antiquated” and badly in need of a shake-up. With the involvement of bankers, insurers, warehouses, customs officials, etc, the processing of trade credit required on average 36 distinct documents, and 27 parties who would spend “hours if not days fact-finding and form-filling”. Fewer than a quarter of banks used electronic documentation. 

Covid and the attendant supply-side disruptions made the incorporation of sophisticated technology, with attendant efficiencies, even more imperative.

In 2022, Tradeteq created trade finance-backed tokens for the XDC blockchain. This does nothing for the underlying transactions, but it does facilitate the packaging of trade finance returns into standardised investments. 

One of the cutting edges of innovation in fintech today is machine learning. Surely this can in principle be applied to trade finance? Algorithms can learn to improve credit performance predictions as new data is acquired over time. McKinsey has estimated that the total size of the “global trade finance ecosystem” is $5.2 trillion, which should certainly incentivise machines to learn. 

But some observers have expressed scepticism about how quickly machine learning will make inroads here. George Souri, founder and chief executive officer of Chicago-based lender LQD Business Finance, for example, says: “As machine learning is learning, it makes a lot of errors, and it needs the right data set. The data set a model like that would need just doesn’t exist. That’s problem one. Problem two, to build it would be extremely costly because you would have millions and millions of dollars of charge-offs.”  

Mike McGill, a portfolio manager at AllianzGI, addressed this point also in a recent interview. “We have observed a lot of activity around blockchain and machine learning applied to disrupt trade finance, but we haven’t really seen anything that scales. We have seen more traction with fintech platforms using technology to reduce unit costs and make trade financing of SMEs more attractive and accessible for institutional investors.”

Drip feed: The portfolio perspective 

Exposure to trade finance can play a valuable part in a portfolio.

As Pushkar Mukewar, chief executive officer of trade finance firm, Palo Alto-based Drip Capital, explains, the returns are uncorrelated. “In the 12-month period ending 30 September 2022, Drip earned a net return of 8 percent,” he says. “In that same period, the iShares 1-3 year Treasury bond ETF lost 3-9 percent.”

Drip is backed by venture capital firms including Accel, Sequoia, and Y Combinator. It offers financing solutions in emerging markets such as India and Mexico as well as to US importers.

Mukewar explains that Drip has developed “a proprietary risk assessment model to evaluate and underwrite risks in cross-border trade transactions. The model combines a risk policy engine and machine-learning based mechanisms that consider direct and indirect data sources.”

Speaking broadly, cloud-driven computing has driven down the costs of using Big Data, and together those two developments allow firms managing funds to match the right SME exporters with the right buyers.