Jan 15, 2021

AI transformation of the global trade ecosystem is underway

AI
trade
Michael Boguslavsky
3 min
In trade finance, AI is particularly helpful in analysing quantitative data, as there are usually many repetitive small transactions
In trade finance, AI is particularly helpful in analysing quantitative data, as there are usually many repetitive small transactions...

Ask the public about Artificial Intelligence (AI) and its application in our lives and some people immediately jump to dystopian, doomsday scenarios where robots have taken over the world. While this has made a good plot for Hollywood, the reality is rather different. What many don't realise is that AI and its subset, machine learning, already forms a central part of our day-to-day lives. 

Those new products that Amazon suggested you add to your shopping cart? AI. That gripping TV series you watched on Netflix via an automated recommendation? AI. That self-driving Tesla car you crave to take for a spin (or rather, takes you for a spin)? Yes, you guessed it – its AI!

Today, there’s not a single industry that is not being re-shaped by technology in one form or another. Until recently, however, there was one noteworthy exception to this: global trade. Fortunately, that too is slowly changing.

The financial mechanism that underpins global trade – trade finance – is a centuries-old industry that remains largely paper-based and reliant on manual processes. This USD15 trillion a year industry is now being influenced by a new wave of technological innovation, including AI. 

The role of AI in trade finance

AI generally refers to the use of computers and computer-aided systems to help people make decisions or make decisions for them. It usually relies on large volumes of data or sophisticated models to help understand the best ways to make sense of all the information and draw intelligence. 

In trade finance, AI is particularly helpful in analysing quantitative data, as there are usually many repetitive small transactions. The nature of trade finance means that there is a lot of non-traditional data at our disposal. This means that when banks and other trade finance providers need to assess the risks of funding a transaction between a business and its counterparty, AI-driven models can be a very efficient tool for data analysis and reveal intelligence and risks.

Crucially, this goes far beyond the traditional credit scoring process, which is often outdated and remains reliant on a small number of historical accounting entries – a major barrier and prevents many small companies from accessing trade finance. In fact, the current short

fall between what banks can lend and what businesses need was around USD1.5 trillion even before the COVID-19 pandemic, 10% of global trading activity!

Transforming the credit scoring process for SMEs

AI can help to tackle this shortfall by creating more accurate credit scoring models that offer deeper levels of intelligence to inform a trade finance provider’s decision. This can include analysing a company’s payment history, measuring the risks of funding a specific transaction when dealing with different counterparties, identifying supply chain risks and benchmarking them against their peer group.

Trade finance providers can use this information to communicate more effectively with their SME clients. This creates more trust between them and establishes better business relationships. For SMEs, this opens up trade finance access for companies that would otherwise not have that access and helps to reduce the trade finance gap.

Tech will continue to shape the future of trade

The adoption of AI is just one of a series of technological advancements that will transform the global trade ecosystem over the next decade. From blockchain-based systems to real-time anti-money laundering and fraud alerts, this industry is in the early stages of a radical transformation.

The timing is not coincidental; these advances are largely driven by a new generation of fintechs that have emerged in recent years. For example, we have seen the industry work together to create a new infrastructure to help banks distribute trade finance assets to other investors in a transparent and standardised format. 

The creation of the infrastructure is only possible due to improvements in modern technology and integration across the trade ecosystem in co-operation with banks, insurers and other long-standing industry participants. 

That is industry-wide collaboration at its best. Together, they are re-shaping global trade as we know it.

By Michael Boguslavsky, Head of AI at Tradeteq

Share article

Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

Share article