Automation

Automation and the Digitalisation of Trade Finance


By Torben Sauer, CEO, and Marc Smith, Founder and Director, Conpend

 

For many banks, trade finance forms a large part of their corporate banking business. As a discipline, trade finance also presents some of the best opportunities for process enhancements in banking, not least because of inherent, manual-based practices and associated inefficiencies – fuelled by the fact that supply chains often stretch into some of the world’s least technologically enabled regions.

As an industry still heavily reliant on paper, billions of paper trade documents are exchanged and reviewed each year, a practice that – while in decline – shows no signs of being eradicated. Indeed, respondents to the International Chamber of Commerce (ICC) 2020 Global Survey revealed that 45 per cent of trade banks reported “no progress” in reducing their paper use for document verification.

While the need for a transition from paper to digital processes is widely recognised in the industry, the complexity of global trade and the sheer number of participants involved within global supply chains can impact the speed of progress. Yet, with the importance of digital reinforced by the remote working conditions implemented as a result of Covid-19, the industry has become acutely aware of the pressing need for change.

With trade finance on a trajectory towards becoming digital, solutions that seek to optimise trade finance must take all stakeholders into account – which includes those that remain reliant on paper-based documentation – thereby supporting not only the needs of today, but the trade finance landscape of the future.

Artificial intelligence (AI) oriented automation – that can read and assess paper-based documentation – could provide an answer. AI technology has applications well beyond trade finance, of course, although it is here where the document checking capabilities of AI can be put to immediate and productive first use.

Automation in document checking

Currently, those undertaking manual documentary checks – usually by a bank operative in a service centre – must laboriously sift through volumes of paper searching for anomalies that include evidence of fraud or sanctions violations.

AI software, however, once programmed, can detect these same anomalies, automatically submitting any findings or queries to an authoriser for review. This is no small improvement. Automating this process results in significant efficiencies, and removes the risk of human error. Indeed, the fines for non-compliance with anti-money laundering (AML), know-your-client (KYC) and/or sanctions regulations are deliberately punitive and can amount to billions of dollars – and human error can be enough to fall foul of the legislation. Given this, it is no surprise that 63 per cent of ICC survey respondents reported they were “extremely concerned” about AML and KYC requirements and see them as a barrier to their growth prospects.

Without the aid of automation, even the most eagle-eyed document authoriser can miss an anomaly that reveals a serious (or even minor) breach, leaving banks vulnerable to unknowingly transgressing AML or sanctions regulations.

Hundreds of pages of documentation can accompany each trade finance transaction. Yet, AI-based document checking can quickly and reliably identify patterns across documents, without the risk of tired eyes making a mistake, all while significantly reducing the review time.

Automation can also help banks keep abreast of a rapidly changing regulatory environment. AI solutions, such as the Trade AI app, are able to check each document against a set of pre-determined rules, which are updated using machine learning (ML) techniques, so that an anomaly successfully dealt with has the resolution applied to all future documents. This steadily improves the review process over time.

It is also worth highlighting that all data drawn from these documents upon their arrival is now in a digital format – optimising downstream data management processes within the bank. Different types of documents are brought together from one end of a supply chain to the other – including invoices, packing lists, bills of lading and certificates – which are converted into a machine-readable format using optical character recognition (OCR). The app makes this rich database available for analysis and insights that can inform decision making and promote growth. And optimisation at the document-checking level increases a bank’s overall level of digitalisation, advancing organisation-wide initiatives to reduce paper processes.

Automation’s human perspective

Advocating for automation also has a human angle. Contrary to common concerns about adopting new technologies, optimisation in this case should not lead to job losses. Rather, automating document checking keeps pace with two of the biggest shifts happening in the labour market.

If AI streamlines the most repetitive and error-prone aspects of document review, human operatives are therefore more effectively positioned to apply and demonstrate their expertise. They are freed to focus on the analytical and decision-making process of the review while hugely increasing the potential flow-through volume reducing delays in the supply chain. Banks can retain and retrain experienced employees that already understand their business and practices while focusing their time and skills more efficiently.

In addition, young people entering the workforce are more computer literate than previous generations and are therefore accustomed to the pace of technological change. The new workforce is well-suited for digital, rather than paper-based, review tasks.

Furthermore, automation enables flexible and meaningful resource allocation. When workload fluctuates, employees can successfully scale their operations because the most time-consuming part of the review has been delegated to automation. Practical restrictions, such as physically delivering documents for office-based review are alleviated –benefiting price-sensitive commodities requiring quick-approval turnaround. Documents are received by operatives digitally, enabling them to work from home, and the kind of time-consuming disruption brought on by the pandemic and, indeed, continuing to reverberate around the world, can be limited.

The future for trade finance and automation

The transition to digital trade finance is a significant challenge. Documentation digitalisation initiatives have sometimes promised too much too quickly, resulting in failure or only partial success. As such, only 17 per cent of the ICC’s respondents believed their digitalisation initiatives were successful, with 22 per cent of banks describing their attempts as imperfect.

Farsighted ambitions envisage fully digitalised trade finance platforms, integrated seamlessly between banks and stakeholders. With the adoption of Model Law on Electronic Transferable Records (MLETR) and related frameworks on the horizon, regulators are lending recognition to digitalised trade.

Yet, this momentum towards a digital finance future can still be hindered by a single stakeholder or port authority without the hardware or technical expertise to produce electronic documentation – potentially bringing a supply chain to a halt. Certainly, while trade processes are not solely within banks’ control, it is unrealistic for banks to wait for all stakeholders to digitise.

The pandemic has highlighted the fragility of trade networks. Persistent regional Covid-19 outbreaks delay both the movement of the cargo itself and the delivery of critical legal documents.

Under tumultuous circumstances, the practical approach to optimising trade finance – through the use of automation – is therefore proven to be the more effective one. AI-based document checking empowers banks to independently increase their productivity and efficiency while planning for and expecting further transformation – indeed, encouraging a digital future.

Of course, trade finance is far from the limit of AI’s applicability with respect to document checking. Automation can streamline many processes where checks are required involving quantities of documentation.

For instance, the time-consuming process of onboarding new customers and suppliers can be streamlined with AI. Again, KYC and AML are major concerns, with some banks responding by de-risking and decreasing business in certain countries or with smaller companies, where the documentary onboarding process may be difficult or less worthwhile. AI can help by directly addressing banks’ biggest concerns – sifting through documentation looking for anomalies. Indeed, should a transaction or counterparty become a legal concern, this process helps protect banks from potential prosecution. Another candidate for automation is loan processing. Where loans are syndicated, participating banks will be able to process the creditworthiness of a borrower via AI.

Yet, it is trade finance documentation where perhaps the most significant progress can be made via the use of automation. And as the global economy recovers from the pandemic, AI could help smooth some of the bottlenecks inevitably occurring.

 

Conpend’s white paper on the subject – Trade finance document checking and data management: the case for automation – can be downloaded here



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