In September 2019, we are launching our new cutting-edge MSc Financial Technology programme. We are excited to welcome the inaugural class who will be future leaders of the fintech industry.
The programme features an engaging curriculum that teaches the quantitative and analytical skills to excel in fintech. In September, students begin with a foundation module which is made up of six components that introduce the tools of modern finance and enhance their programming knowledge.
This sets them up perfectly for the core modules which run across the autumn, spring and summer terms. These modules are across the key areas of financial technology and give students an intensive grounding into the skills they need to launch their careers in the industry.
We speak to faculty members who will be teaching on two of the core modules; Tarun Ramadorai, Professor of Financial Economics who will be teaching Big Data I, and Ansgar Walther, Assistant Professor of Finance teaching Big Data II.
Tell us about your academic background and your interest in fintech?
Tarun: I did an undergraduate degree in mathematics and economics, I have a Master’s in economics and a PhD in business economics. During the course of my research, I’ve done a huge amount of work in computational applications, working with data in particular. This means large empirical projects using very large amounts of data from all around the world.
This has led to an interest in the use of large datasets, whether that’s structured or unstructured. And it’s also led to the use increasingly of what is now known as machine learning and artificial intelligence types of techniques. This is just becoming part of the standard toolkit that you have to use to do cutting edge research these days. And so that’s led to a natural development of interest in the area of fintech.
Ansgar: I have a PhD in Economics from Cambridge and have held academic posts in Economics and Finance at Oxford, Warwick and Imperial. Having spent most of my career studying financial systems and the economics of information, I am obviously fascinated by fintech and its impact on the economy. I think technology has huge potential to make the financial system more efficient, and perhaps even more importantly, fairer and more accessible for everyone.
For example, a well-known issue is that many households who are not wealthy lack access to credit and do not participate in stock markets. Fintech lenders and investment advisors have the chance to change this. At the same time, we need to be careful that a tech-based system does not penalise or deny access to people that are already disadvantaged. These issues motivate most of my research at the moment, and I am collaborating with both policy institutions and fintech firms to gain new insights.
What can students expect to learn on your core module?
Tarun: Over the last few years, there’s been an explosion of interest in the use of large datasets and new empirical techniques that are being used to facilitate financial decisions that people make. So what we do in Big Data I is examine how a combination of large datasets, new empirical techniques including machine learning, and insights from fields like psychology and behavioural finance are combining to help market participants make more effective financial decisions. We will focus on credit and mortgage analytics as well as the use of big data techniques and asset management strategies. Those are two particularly important areas that students can expect to learn a lot of techniques about.
Ansgar: Big Data II, not surprisingly, follows on from the prerequisite module Big Data I. The philosophy for both modules is the same: We want students to learn techniques to analyse big data, such as machine learning models, and at the same time practice applying these tools to financial problems. On the technical side, Big Data II puts more emphasis on deep neural networks and reinforcement learning. We will apply these tools, among other things, to the core problems of asset management, portfolio choice and trading strategies.
How does your module equip students with the skills to pursue a career in the fintech industry?
Tarun: This is now my third year teaching Big Data I at Imperial, having taught this module previously on our other Finance Master’s programmes, and these skills have become extremely useful. Many of my students have come back and reported to me that this module was very helpful in interviews they are now having. It’s also the case that the skillset that’s expected of them has changed and evolved so that some of the techniques that we teach them are becoming particularly important. This is an important module in the sense that it will open the door to a whole set of areas that they might not otherwise have thought they were equipped for. But also increasingly the areas they thought they were equipped for, or are requiring facility with, require some of these new tools and techniques. It’s an essential part of the toolkit, if you don’t have it you can suffer.
Ansgar: The skills students learn on this module are directly applicable for anyone wanting to work in a fintech business that involves credit scoring or asset management. Some concrete examples of firms that need to hire people with these skills are hedge funds, peer-to-peer lenders and “robo-advisor” companies. But the skills are valuable much more widely. For example, neural networks and related techniques are used extensively to prevent fraud in the financial industry, to automate consumer service interfaces, or to optimise marketing strategies. Further, throughout the module students will have guest lectures where they get a chance to interact with people from the industry who are looking for talent.
Why is Imperial the place to study a Master’s in Financial Technology?
Tarun: Imperial has an illustrious history, it’s one of the best STEM schools in the world. It’s routinely in the top 10 in the world university rankings and it’s got a very impressive heritage in the areas of science and technology. We also have a world-class finance department. A combination of both these factors makes this the ideal place to do work in this particular area. Fintech is one of those very fast-growing clusters of activity and organisations are hiring some of the smartest people in this subject. Student quality on our Finance Master’s programmes has been very high, our finance PhD students are particularly bright and working on very innovative areas. Imperial is a really exciting place to be.
Ansgar: The unique feature of Imperial College is that we have a world-class finance department next door to world-class engineering and computer science departments. We have the perfect ecosystem for teaching and researching fintech – for example, if I am applying financial tools to big data and want to know the state of the art processes, there are literally dozens of elite tech researchers that I can talk to. Very few places can offer this combination.