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The New Brains in Trade Finance

The pace and proliferation of technology in every area of business has reached such a future that the emergence of the digital age almost seems like providence. Trade finance has, to some extent, been left floating in the wake of progress. But, with the evolution of artificial intelligence, the sector is primed to make the same leaps forward with fintech innovations springing up all over the place. This article has a look at the ways and means by which AI in particular is carrying trade finance forward

Educating Machines

AI is now firmly ensconced in wider social consciousness. It was embedded there initially through literature and then film, but the coming-of-age of the digital era post-WWII has converted the subject from fiction to a science. Wikipedia has AI as being intelligence that is demonstrated by machines as opposed to that shown naturally by biological organisms.

One of the more integral aspects of machine-based manufactured intelligence is that of learning. Where a machine or piece of software may be programmed to carry out tasks in a repeated or rote fashion, the learning aspect applies where said program or machine can exceed the original limitations of its program through education and experience.

According to Trade Finance Global and shown in fig. 1, the critical learning facet of manufactured intelligence can be divided into a number of categories. Machine Learning is essentially a set of directives that foster performance and understanding.

A machine or piece of software can evolve its comprehension of a subject and even enhance the ways it continues to learn.

Neural Networks are connections that, in many ways, mimic the cells and neural pathways found in the biological world. Deep Learning is a mix of machine learning and neural networks: complex problems can be solved through using multiple network layers and the machine/software’s enhanced learning. Language Processing is where machines or software are able to communicate with humans they way human beings communicate with one another.

Brought together, these elements allow AI to evolve in such a way that a biological intelligence would. Globally, artificial intelligence (AI) technology has infiltrated every financial services sector.

In emerging marketplaces, financial service providers are able to automate their enterprises and utilize new and vast tracts of data to solve once thought to be insurmountable problems. Where once something as simple as geography was a major hurdle – where impoverished regions made providing services to rural and low-income consumers expensive and lacking in security – now, technology has opened up these new vistas for business and inclusion has become a wider possibility.

The onus too is on organisations in the financial space to adopt and use the new technology responsibly and sustainably, not to mention to continue to invest in research, development, and infrastructure

Analytics and Automation

Currently, AI is particularly useful when applied to emerging or immature marketplaces. Biallas and O’Neill point out (for the IFC and as a member of the World Bank Group) that one of the first wider applications of AI is in the analytics of alternative datasets and live information sources. These approaches bolster decisions made around credit assessments, improves compliance by enriching the analysis of the potential of risks and threats to a given organization, and addresses financing gaps within the space. In turn, AI also enables automation. In engaging with customers and the application of business models, AI is able to capture a more copious and varied consumer base without the attendant increased costs. And all the while, automation reduces the error and misunderstandings that can seep in through human-supervised activities.

A profound and well documented issue that continues to dog trade finance is the use of paper for record keeping. As pointed out by Sauer from an International Chamber of Commerce report in an article from November last year, 45% of surveyed banks have reported no progress in reducing paper-based transactions.

Considering the sometime convoluted contracts that can take place in international trade (looking at you Mr. Supply Chain), the amount of documentation needed can grow to be burdensome. This alone creates a formidable impediment to digitizing aspects of the sector. Compliance, regulation, and manual supervision usually slows processing down. Where AI can step into the fray is in the scanning of the volumes of paper.

With predefined parameters, it can read and interpret the documents and search for breaches, irregularities, or outright deceit. Through its inherent machine learning, AI can also stay abreast of new developments and evolve as the industry does

Figure 1: Components that make up artificial intelligence (as described by Trade Global Finance)

Rise of the Robots ?

There have been some concerns raised as to the diminishing human element in trade finance transactions – most notably job losses – with the rise of AI and automation. Sauer and Smith point out for International Banker that this need not be the case at all. Automation and optimization are synonymous with key changes that are happening in the labour market. As touched upon above, AI streamlines repetitive, tedious, and mistake-laden areas of trade finance, especially document scrutiny.

Where AI steps in and takes over, trade finance professionals are better placed to conduct analyses, make decisions, and speed up the entirety of the process. Moreover, younger people entering the workforce tend to be more computer savvy and are able to embrace technological changes more readily. Generations coming through will be more orientated toward a digital working environment than the ones before. Further still, resources become more dynamic with automation of this kind as when workloads fluctuate, personnel can be shifted around to fill gaps. On the whole, AI used wisely should enhance – not detract from - the existing workforce.

As alluded to by Ballias and O’Neill, in light of the constant progress being made in AI-based trade finance, risks posed by it must also be kept in focus. As with AI used in other industries, there are sector-driven privacy and algorithmic bias challenges. The International Committee on Credit Reporting has established ongoing threats that require the right supervision. These are inaccuracies of data, the use of data without informed consent, baked in discrimination in the use and design of AI algorithms, and increased vulnerabilities to cyber attack. New measures for the supervision of business operations around data ownership, privacy, and security will require development.

Equally, early adopters of AI technology within the space may inadvertently or consciously look to leverage their advantage by generating larger and larger datasets to further refine and train their algorithms. As with other technology, monopolies could form and competition in the marketplace be reduced. While new technologies emerge, business will also need to attract the right talent to fully understand AI and engage it responsibly: there is the possibility of wider financial liability placed on vulnerable consumers which in turn could erode trust in the industry.

For the time being, the scope of AI in the industry is narrow. However, new opportunities are always being identified especially as the technology itself develops. In tandem with this, new guidelines and legislation are being devised to keep the industry responsible and sustainable. Awareness and education are expanding with the growing ubiquity of the technology and new infrastructure continues to emerge and be subject to review.

About US

Nu-Credits is a trade finance market place making trade finance accessible to credit worthy SME’s by connecting them to global lenders. We provide blockchain infrastructure and integrated data solutions to assist lenders in underwriting trade finance credits to SME’s

Nu-credits differentiates itself by addressing major pain points in the trade finance industry through its block chain enabled platform such as credit risk management, document collection process, technology adoption, business capacity, client engagement and external environment instability

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