In the past decade, we have seen remarkable breakthroughs in the areas of machine and deep learning, and their applications in a wide range of tasks, from image classification & video processing to speech recognition & natural language understanding. The data in these tasks are typically represented in the Euclidean space (i.e. they follow the rules of Euclidean geometry, e.g. the shortest distance between two points is a straight line). However, there are a number of applications where data are generated from non-Euclidean domains and are best represented as graphs with complex relationships (e.g. client accounts in a bank, interactions between these accounts, and the inflow and outflow of assets from these accounts). Consequently, many studies on extending deep learning techniques for graph data have emerged. There are indications that these emerging techniques perform significantly better than traditional methods on non-Euclidean data (e.g. rules based or statistical approach). Given their relevance to areas such as prevention of money laundering and financial crime, in this post, I have provided links to a couple of introductory talks on this topic. Enjoy!
Millennials are a unique generation caught in a faltering global economic system that has already peaked productivity and taken us to the precipice of climate change. They are in despair as their values are not represented in the economy or governance. A seismic shift in the world economy is also underway. According to author and futurist, Jeremy Rifkin, we are in the final phases of the fossil fuel era. Much as coal and steam powered the First Industrial Revolution, and oil and telephony powered the Second Industrial Revolution, clean energy and digital technologies are now converging toward what he describes as the Third Industrial Revolution. This next phase of infrastructure modernisation is rooted in the convergence of 5G, a renewable energy Internet (clean technologies and smart grids), and a digitised mobility and logistics platform (autonomous electric vehicles, artificial intelligence, and the Internet-of-Things).
Legal documents play a vital role in protecting the interests of a business and the business owners over the course of a company’s lifetime. Documents such as master agreements, shareholder or partnership agreements, and memorandum of understanding specify how a company’s business affairs should be organised. In Wholesale banking, for example, the contractual relationships that a bank has with its clients dictate how the bank should calculate the daily margin calls, estimate its counterparty credit exposures, and whether a client is entitled to the client money protection rules or not. It is therefore vital that audit professionals pay close attention to legal documents and provide sufficient assurance that a company’s day to day business affairs are being conducted in accordance with its legal and contractual obligations.
Facebook recently announced a new digital currency called “Libra”. The company is partnering with 27 other businesses and organisations to start the crypto as an open-source digital currency. Facebook says that it aims to make it as easy to send money around the world as it is to send a selfie or a WhatsApp message. Libra represents a huge step forward for the entire digital currency space, but is it actually like a Bitcoin, Ethereum and Ripple or a traditional fiat currency like USD, Euro and GBP? And what does it mean for the banking industry? Let’s explore!
It is not enough to just import technologies like Robotic Process Automation, Artificial Intelligence, Blockchain or smartphones into existing financial services. To stay in business, banks need to rethink the role their business plays in their customers’ lives. It is with this intent that the Financial Services industry and FinTech startups are innovating across broad categories – in investment management, payments, insurance, credit, and even money itself. So what’s driving this change? And what makes it possible? This video recording of a talk by Gregory La Blanc (Lecturer – Berkeley Haas) offers a brief tour of how and why the financial markets and institutions are rapidly changing.
A core topic in Machine Learning is that of sequential decision-making. This is the task of deciding, from experience, the sequence of actions to perform in an uncertain environment in order to achieve some goals. Sequential decision-making tasks cover a wide range of possible applications with the potential to impact many domains, such as finance (intelligent algorithmic trading), robotics, healthcare, self-driving cars, and many more. Inspired by behavioural psychology, Deep Reinforcement Learning (RL) proposes a formal framework to this problem. Deep RL has also started to receive a lot of attention since the January of 2016, when a team of researchers from Google built a Deep RL based AI that beat the reigning world champion of the board game Go. So what is Deep RL? Let’s explore!
Around the middle of last month, Elon Musk dramatically twitted that: “On April 22, Investor Autonomy Day, Tesla will free investors from the tyranny of having to drive their own car”. Tesla live-streamed that event and made a video recording available on YouTube. Having just finished watching this video, I am blown away by the progress that Tesla has made in making that dream of a fully autonomous self-driving car a reality (not in the distant future, but in 2020!!). There is an incredible amount of AI hardware and software innovation that has gone in to make this possible, including using targeted video snippets collated from existing Tesla cars driving around the world for improving the predictions of the neural network that powers the self-driving software, to building a computer that does just one thing (and only one thing) very well – i.e. self-driving. So if you are interested in self-driving cars or AI in general, I will highly recommend watching this video.
Whether you get a job or a mortgage, who you date or where you eat – algorithms increasingly determine the big and small decisions in our lives. We may not be aware, but behind the scenes, many companies, and increasingly governments too, are using or plan to use algorithms to automate bureaucratic processes and business decisions. Because algorithms are faster and more efficient than people. But do they always make better decisions? Can algorithms be misused for monetary gains at the cost of others? With these questions in mind, this article explores the darker side of black box algorithms that we as a society must address as our lives become increasingly intertwined with modern digital technologies.
What are Chatbots? Where did they come from? What are they good for? And of course the million dollar question – how do you monetise Chatbots and conversational products? Let’s explore!
Technology has had a transformative impact on our everyday lives, and continues to reshape the way businesses operate and interact with their customers. Yet the legal and regulatory industries have not fundamentally changed – at least not yet. But this is about to change. The rise of LawTech and RegTech promises to not only transform these centuries old professions, but also enable us to completely rethink and redesign the very basic concepts of trust, contracts, regulatory compliance, and access to justice. With that in mind, this introductory article explores how RegTech and LawTech are increasingly becoming central to overall digital transformation and compliance strategies.