NextGen Nordics will bring together multiple stakeholders - the banking community, central banks, public authorities and trade and business beneficiaries - to generate community discussion, explore the benefits and offer practical advice on activating the opportunities of new technologies in the Nordic region. Danske Bank revealed plans to digitise its trade finance process through a collaboration with Conpend for use of its Trade AI app. The app aims to streamline existing processes, enhance efficiency and transparency, reduce the probability of error and enable an easily accessible end-to-end trade finance transaction audit trail. The app was developed specifically to encourage automation and digitisation of the value chain while accepting the current situation with respect to documentary checking. Paper-based documents, as well as digital documentation, can be scanned and entered into the application, which'reads' the contents and checks them against a set of pre-defined rules.
The menace of trade-based money laundering (TBML) is an increasing, yet often under-reported, financial and reputational risk to banks and a growing concern to governments and regulators. Transnational crime is worth up to $2.2trn each year and much of it is facilitated by various forms of trade-based money laundering. A PWC report stated that 80 percent of illicit financial flows from developing countries are accomplished through trade-based money laundering. With sums of this magnitude, it is not surprising that banks, who are often the (unwitting) facilitators of this illegal activity, are coming under increasing pressure from regulators to take greater action to limit this growing international crime. For banks, TBML, disguised under the huge volumes of legitimate trade, is also extremely difficult to detect.
Machine learning in finance has become more prominent recently due to the availability of vast amounts of data and more affordable computing power. Machine learning in finance is reshaping the financial services industry like never before. Leading banks and financial services companies are deploying AI technology, including machine learning (ML), to streamline their processes, optimise portfolios, decrease risk and underwrite loans amongst other things. Here in this article, we will explore some important ways machine learning is transforming the financial services sector and examples of real applications of machine learning in finance. To answer this question and understand the role of machine learning in finance, we must first understand why machine learning is suitable for finance. Machine learning is about digesting large amounts of data and learning from that data in how to carry out a specific task, such as distinguishing fraudulent legal documents from authentic documents. Machine learning in finance is the utilization a variety of techniques to intelligently handle large and complex volumes of information. ML excels at handling large and complex volumes of data, something the finance industry has in excess of. Due to the high volume of historical financial data generated in the industry, ML has found many useful applications in finance. The technology has come to play an integral role in many phases of the financial ecosystem, from approving loans and carrying out credit scores, to managing assets and assessing risk.
Singapore has officially launched a centralised data platform that it says will streamline information flows across a fragmented global supply chain. The data exchange already has garnered at least 70 participants that include logistics operators, banks, and energy companies. Called Singapore Trade Data Exchange (SGTraDex), the common data platform was first introduced in November 2020 as a pilot that aimed to improve data efficiencies in container flow and financial processes. The project is led by Alliance for Action (AFA) on Supply Chain Digitalisation, one of seven industry groups the government had established to identify and prototype new ideas to drive the local economy. The other alliances focus on key growth areas such as robotics, e-commerce, and environmental sustainability.
We live in a data-driven world. Exemplifying that, the global data sphere will grow from 45 zettabytes in 2019 to 175 zettabytes in 2025, according to a study by International Data Corporation (IDC). In many sectors, organisations are recognising that if they are to deliver real business value and remain competitive in a constantly changing marketplace, it is imperative that they invest in the ever-better management of their business processes. In the financial sector, for instance, Deloitte estimates that the volume of unstructured data is nine times greater than that of structured data because new applications for accounts and loans require ever more verification. So, for pretty much any future-thinking company, the only way to remain effective is to make the best possible use of technology to process the growing deluge of data.