We see “100% recyclable” labels everywhere – on yoghurt pots, containers, and plastic bags. But are they true to their promise? And how can we work to promote plastic recycling not just among individual consumers, but more widely in industry? Dr Chao Liu, Lecturer in Decentralised Finance and Blockchain at IFT, presents the issue and the Plastic Credit system he has devised.

Lightweight, cheap to produce and durable – plastic has become embedded in our daily lives and universally throughout industry. There are many types of plastic materials used across a range of products. PET is the most common material for clear plastic bottles, for example, and PP can be used for sealing film, packing tape, and single-use straws.

Manufacturers use a circular triangle with a number inside to identify plastic type as shown. Each type of plastic material is identified as widely recyclable, recyclable, or not recyclable. But what do “Recyclable” and “Widely Recyclable” actually mean? Is it 10%, 50%, or 90% recyclable? And what happens to non-recyclable material during processing?

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If ‘data science’ is ‘statistics re-invented’, then arguably artificial intelligence must go further than that to differentiate itself. IFT’s Director of Research, Professor Sir Alan Wilson, reflects on the overlap between data science and AI, and what more it can offer. Prof Wilson reads this as the ability to offer insights from the analysis of large data sources or ‘big data’ that goes beyond statistics.

An introductory step is to sketch what AI can do and what it can’t do. Through associated methods in mathematics, statistics and computer science, combined in new ways, AI can see, hear, read, translate and write. These are remarkable achievements and can themselves transform many business processes. Many of these are being applied by banks, for example, in serving their customers. What AI can’t do is ‘think’. This led Michael Jordan to argue, in his blog post Artificial intelligence: the revolution hasn’t happened yet, that we should neglect the earlier ambitions to create a human-imitative ‘thinking’ AI, and convert AI to IA: intelligence augmentation.

If we continue to follow Jordan, we can note that much of AI is ‘machine learning’. This allows us to identify hitherto undetected patterns or structures from data, thus providing insights and augmented intelligence. This can be dramatic: DeepMind’s AlphaGo systems which recently showed how to reveal the structures of protein folding is a remarkable case in point. The simplest kind of structures are clusters, for example of population types, which provide the basis of new marketing algorithms. Another kind of product is ‘anomaly detection’ which has obvious applications in finance in relation to money laundering and fraud.

There are three kinds of machine learning: unsupervised, supervised and reinforcement learning. Unsupervised throws machine-learning algorithms at large databases and invites the delivery of possible clusters; supervised learning starts with a cluster-labelled data set and seeks to position elements of new data into these clusters; reinforcement learning combines new data with old in such a way that the cluster definitions can be improved – and in this sense, the system ‘learns’ as data is added.

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Financial markets have undergone a deep reorganisation in the last 20 years. A mixture of technological innovation and regulatory constraints has promoted the diffusion of market fragmentation and high-frequency trading. In this blog, IFT PhD student Zihao Liu reports on the inaugural seminar in IFT’s Agora Seminar Series – “High frequency trading and networked markets” with Professor Rosario Nunzio Mantegna.

Due to the high-speed development of FinTech and technical innovations in recent years, the operation of the global financial market has changed beyond recognition. Ever more market participants are beginning to use powerful computer algorithms to execute and complete orders in mere microseconds. The new stock market has changed the traditional ecology of market participants and market professionals.

With the development of strategic trading decisions based on high-frequency trading, the fragmentation of markets has occurred. Contemporary stock markets are now “networked markets” where liquidity provision of market members has statistically detectable preferences or avoidances.

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The Institute’s USP is rooted in linking research in finance and technology – the latter in two senses, technology in finance itself, and technology in the wider economy – the customer of financial services. A subsidiary aim is to link academic research with industry and the public services. This stance demands interdisciplinarity and it is useful to explore what this means. In this contribution, our Director of Research, Professor Sir Alan Wilson, presents the framework for interdisciplinarity offered in his recent book, Being interdisciplinary.

A first step is to define a system of interest for a research project. In broad terms, this will demand specifying the components of the financial services ecosystem, and those of its customers that are relevant to the project. To fix ideas, consider a project to explore the maximisation of ESG objectives in portfolio construction by an asset management company. The system of interest is based in the elements of the portfolio and hence the wider economy, risk and uncertainty, the companies own market, and the elements of ESG to evaluate those dimensions of the portfolio. The drive into interdisciplinarity comes from posing the question: what is the requisite knowledge base needed by the company to be efficient and effective? This will embrace all the elements of portfolio management (and hence mathematics and statistics), the companies represented in the portfolio (economics, geography and business – national and international), the government and regulatory context (hence politics and public administration), and the elements of ESG (environment, including climate change, the social impacts of investment, and governance – business again). This is a huge agenda, demanding both breadth and depth in the company’s staff and access to top-class reference material. Parcelling the knowledge into disciplinary siloes will be a very inefficient way of handling this hence the need for interdisciplinary teams. There is a big challenge here that can be research-informed.

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