Over the past two decades, trading in financial instruments has seen a remarkable evolution from open outcry trading floors to on-screen trade booking and execution all the way to algorithmic and high-frequency trading. The success of machine learning and artificial intelligence (AI) seems like a natural progression for the evolution of trading. With that in mind, this article explores some of the practical examples where machine learning is already being used today in financial institutions, and the challenges in building intelligent autonomous trading systems.
I was recently watching a video clip on how a Japanese technology startup is using artificial intelligence to identify upcoming fashion trends (by analysing real-time images), and provide retail consumer brands an edge to speed up their inspirational design phase, help designers and fashion houses better personalise offerings and meet customer expectations. One of the main techniques used by this technology startup is image recognition and classification – an area where machine learning algorithms perform extremely well. There are of course many other amazing use cases of this technology, and if you know how to implement it, perhaps you too can launch your own cool startup?!
To that end, this article is a first in a series of hands-on introductory tutorials on how to use TensorFlow (an open source machine learning framework) and Python to create some amazing tools, such as a fully automated solution for image recognition and classification. In this very first tutorial, you will learn how to build a very basic neural network (with the least amount of coding possible) and train it to recognise images of various articles of clothing and accessories – such as shirts, bags, shoes, and other fashion items. There are of course other deep learning networks, such as the CNN, which will perform significantly better in this task. But to begin with, let’s just learn how to implement a simple neural network, and perhaps in a later tutorial, we will explore some of the state of the art solutions.
A couple of weeks back, I wrote an article about “Machine learning applications in banking”. In this article, I take that discussion a step further by focussing on what some of the industry leaders, policy makers and supervisors in the financial services sector have to say about emerging technologies, such as machine learning and artificial intelligence, and how they see the future of the industry evolving as more and more financial institutions adopt these disruptive technologies.
The banking industry is in a digital arms race. In 2018 alone, it is estimated that Wholesale banks globally invested approximately US$9.7 billion to enhance their digital banking capabilities in the front office. For retail banks, online and mobile channels have become equally important as their ATMs and branches.
Banks around the world are already realising how investments in digital technologies could benefit customer acquisition and satisfaction. But satisfaction is relative. As leading technology brands, such as Apple, Amazon, or Google, have become the gold standard for digital engagement, many consumers now have a stronger emotional connection with these brands than they have with their banks. If banks want to keep up, they have to manage the digital experience they offer to make these emotional connections, which, ultimately, could translate into sticky interactions and more profitable customers.
Against such a backdrop, this article explores how some of the pioneers in the banking industry are embracing machine learning and AI to strengthen their digital capabilities.
According to a recent study of GitHub, the de facto online code repository, Python came up as the number one programming language for machine learning and is also the third most commonly used language on GitHub overall. The same study also identified Tensorflow followed by scikit-learn as being the most active and popular machine learning projects on GitHub. Tensorflow, which was developed by the Google Brain Team, is an open-source programming library widely used for machine learning; and scikit-learn is the native Python library for machine learning.
The results of this study are not surprising to those of us who have done some amount of machine learning related development work. But if you are new to Python, Tensorflow and scikit-learn, and would like to invest some of your time and energy to learn how to use these tools in your own machine learning projects or to even beef up your CV, which book should you consider reading? Here’s my “one book” recommendation.
Fingerprint scanner is a popular security barrier which can be found in all sorts of high-end mobile devices that are currently on the market. It is fast and easy to use as an alternative to those hard-to-remember passwords, unlock mobile devices and apps, and even authorise card payments by simply tapping the screen of a device with your finger.
But is it secure? Apparently not as much as we would like to believe!
You have probably heard of machine learning, but are you sure you know what it is? If you are struggling to make sense of it, you are not alone. There is a lot of buzz that makes it hard to tell what’s science and what’s fiction, what’s rebranding and what’s new. This article is an attempt to provide a simple non-technical explanation of machine learning and why it matters.
The traditional approach to tackling the problem of payment card related frauds is to use a set of rigid rules and parameters to query transactions, and to direct the suspicious ones through to the fraud department for human review. Rules are extremely easy to understand and are developed by domain experts and consultants who translate their experience and best practices to code to make automated decisions. But when a rules-based fraud detection system gets operationalised, one starts with say 100 fraud scenarios and 100 rules to handle it. As time goes by and as the fraudsters change tactics, we encounter more and more fraud scenarios and start adding more rules to keep the number of false positives and negatives under control. There comes a point where nobody really knows or can measure how well the rules work or how many exceptions there are – this is the situation today with a lot of legacy hand-crafted and rules-based fraud and anomaly detection systems.
But do we have a better alternative? The answer is yes, we do, and it is called Machine Learning!