What are the problems that Tradeteq solves for its clients? Tradeteq is a smart technology platform powering global trade investments from end-to-end. Trade finance is arguably the oldest banking product, although also the only one that is not easily accessible for institutional investors. We have the technology to make trade finance investable. For those unfamiliar with trade finance, it may not be immediately obvious why institutional investors should consider putting funds into this asset class, nor indeed why those lending to companies trading goods globally do not simply retain the instruments on their books.

As a set of technologies based on edge computing infrastructure and 5G or high-speed connectivity, it is rapidly becoming a reality with many real-world implementations. Statista estimates global IoT spending will hit 1.1 trillion dollars this year, predicting the number of IoT devices worldwide will shoot up from 13 billion currently, to more than 29 billion in 2030. Since it is based on "things", its direct impact on the day-to-day work of senior management or finance departments may seem remote. But IoT can sustain AI (Artificial Intelligence) and machine learning (ML) applications handling multitudes of data flowing from many types of sensors and devices, even when mobile. It opens up huge opportunities for everyone, whether supplying or using services.

Machine learning and artificial intelligence might be the future of everything in the Fintech sector. Generally, integration of AI improves results since the technology applies methods derived from common aspects of human intelligence but is beyond human scale. In this context, AI empowers business processes by providing a deeper understanding of customer needs. The adoption of technology in this sector has substantially made banking easier. People can now carry out major bank-related tasks online, mainly from any device that has an internet connection.

The archaic, paper-based world of trade finance looks set to take a further leap into the digital future, as trade finance distribution platform Tradeteq begins a collaboration with the Singapore Management University (SMU) to explore quantum computing-based solutions for the industry. Supported by the Monetary Authority of Singapore (MAS) under its artificial intelligence and data analytics (AIDA) grant scheme, the research project, titled Exploring the advantages of a quantum system for machine learning applied to credit scoring, aims to build a predictive machine learning model implemented on a quantum computer in order to tackle inefficiencies in approving trade finance. "Currently, many small and-medium-sized businesses are unable to grow their companies due to a lack of funding as they are deemed'too risky' by current credit rating models," says Pang Hwee Hwa, dean of the SMU School of Information Systems. "With shorter processing time, more businesses could be scored and with greater accuracy thereby creating more trust and providing greater access to finance for companies than ever before." Quantum computing is still very much in its infancy, and the technology doesn't yet exist to build a large-scale quantum computer.

Can't code, or speak Bahasa? Didn't go to school with a CEO's son or daughter? A robot will take your trading seat. Read on if you want to save your job. The threat from automation is in the flows part of banks' global markets business, the most important chunk of the biggest division of investment banking.

Most data turns out to have a greater value than the sum of the parts. There's a story about a global courier firm that said it saw a large drop off in its monthly orders at some point in 2007, not too long before the bottom fell out of the global economy. Traditional economic forecasting did not see an issue, but had there been some visibility into trade finance data at that time it would have shown many contracts had been cancelled, affording us some warning of what was ahead. Macro-economic forecasters and statistical analysts know that trade data provides the most precise window into the global economy there is. Trade finance data has always been a notoriously opaque part in the supply chain, but we are now seeing end-to-end digitisation, as a multitude of banks and software providers test out trade/supply blockchains and other digital platforms.

Lenders in the U.S. overwhelmingly plan to expand financing activity this year. According to new research from Magilla Loans, which offers an anonymous search engine data for both home and business loans, a survey of mortgage and business lenders found plans to originate more than half a trillion dollars' worth of loans in 2018. According to the report, 88 percent of lenders plan to lend more in 2018 than they did in 2017, and more than a third (36 percent) said they plan to lessen requirements for loan approval. While the data included business loans, Magilla noted that mortgages are the main driver behind this trend. So while lending volume in the U.S. might be expanding, gaps in business finance – especially among SMBs – remain.

Today, consumers can quickly order the product they want from their mobile devices or digital assistants (such as Amazon Alexa). These technologies have drastically reduced the time from impulse to buying for a consumer. However, the trade finance and international supply chain that is critical to completing the fulfillment cycle has not yet caught up with the pace of our digital consumers. It is complex, slow, and ripe for digital disruption -- 200 documents including contracts, customs clearance, and shipping are involved and exchanged among the supply chain entities, according to Maersk, the global transportation and logistics giant. Disruption is here: the confluence of three digital technologies -- big data, artificial intelligence, and blockchain -- have the potential to drastically reduce the complexities of trade finance and international supply chains.

With 2017 coming to an end, we can get a glimpse of what's yet to come in the next 12 months through the number of investments and product testing. It's no surprise that financial institutions were the first players to experiment with blockchain technology since 2014 and became active investors in the space that totalled $4.5 billion of funding in 2017. In the past few years, financial institutions were testing blockchain technology and working on proof of concepts. By 2017, this largely moved into production stages. In 2017, a group of 11 international banks developed a trade finance application using R3 technology.

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