Can machine learning transform trading strategies in financial institutions?

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.

The ethical dilemma of using artificial intelligence and autonomous technology

We live in an age of rapid technological advances where artificial intelligence is a reality, not a science fiction. Every day we rely on algorithms to communicate, do our banking online, book a holiday – even introduce us to potential partners. Driverless cars and robots may be the headline makers, but artificial intelligence is being used for everything from diagnosing illnesses to helping police predict crime hot spots. As machines become more advanced, how does society keep pace when deciding the ethics and regulations governing technology? To address this question, this article explores the ethical dilemma surrounding the use of artificial intelligence and autonomous technology.

Building a retail bank ….. from scratch!

Monzo has no branch and its users can only bank using a smartphone app. With over a million customers and nearly 178 million transactions last year, Monzo is one of the fastest growing bank in the UK. A recent survey by the “Which?” magazine puts Monzo at the top of the chart for customer satisfaction. So what’s the story behind this four year old digital bank? Let’s take a look.

How artificial intelligence is shaping the future of the finance industry

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.

Machine learning applications in banking

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.

Automating the Data Scientist – is there any such thing?

Data science skills are likely to be more readily available within the next decade or so. If you are not convinced, just have a look at the list of engineering degrees offered by most universities today – you will almost certainly find at least one curriculum related to data science. But while we wait for the next generation of engineers and data scientists to finish their university degrees and gap years, in the meantime, shortage of data science skills in the workforce will continue to pose a major challenge for organisations in realising the potential of their data. So the obvious question is: why don’t we use machine learning techniques to create our own data scientists? It turns out that some data scientists have already been working on this idea in the recent years. Their objective: develop automated machine learning methods and processes that can make machine learning (ML) more readily available for non-ML experts, to improve efficiency of ML and to accelerate research on ML.