Q&A with Dr Alex Backwell, AIFMRM Senior Lecturer

Dr Alex Backwell has been with AIFMRM since its inception in 2014 – first as a student and now as a senior lecturer and researcher. His career has grown as the institute has developed into a world-class centre for research and scholarship – giving him plenty of scope for international collaboration and cutting-edge research.

You have been with AIFMRM since its launch in 2014?

Yes, indeed. Back then, it was much smaller and still very much in its infancy in terms of scope and vision. Interestingly, if I had been at UCT one year earlier, I would not have been able to do the MPhil specialising in Mathematical Finance. I was among the first intake of students on the programme, and it was primarily due to the energy and passion of AIFMRM founder Professor David Taylor that I wanted to stay. Over the years, the institute has grown in stature and recognition with key relationships in industry and a vibrant research community that I am excited to be a part of.

What is your current role at AIFMRM?

I am a Senior Lecturer. I teach on both our degrees, the MPhil in Mathematical Finance and the MCom in Risk Management of Financial Markets, the former being the very programme that I took about seven years ago! My other priority, in addition to teaching, is academic research. My research focus is on the interest-rate markets, especially the modelling of interest rates and their volatility.

Could you perhaps expand a bit on your particular field of interest and research?

Certainly. My PhD thesis was about the mathematical modelling of interest rates and particularly the modelling of interest-rate volatility, that is, the potential variation of interest rates. At the moment, for instance, volatility is high in most financial markets, due to COVID-19 and the turbulence it has created. The interest-rate markets have been greatly affected, because of greater demand for simpler and lower-risk investments. The ultimate aim of interest-rate and other financial modelling is to understand and manage the risk associated with developments to the market and economy, both expected and unexpected. One of the potential new research areas I am interested in, in addition to continuing my PhD work is around a publication in the Journal of Finance by Luigi Zingales from the University of Chicago on Does Finance Benefit Society? This type of work has interesting implications, I think, especially in our emerging market context. Another area I’m working on is the replacement of LIBOR (the London Interbank Offered Rate) as the central benchmark interest rate.

You describe yourself as an academic at heart. What does this mean to you?

I suppose it boils down to being primarily interested in ideas and concepts. That is not to say that I am not concerned with how theory plays out in practice, or that practical and applied matters are not important, but I enjoy abstract thinking and building and expanding on current models of thought and academic theories.

Have the COVID-19 lockdown restrictions affected your way of working?

Not very much. As long as my internet connection is working, I can teach online and interact with other researchers and publications to discuss all aspects of my work. Connecting with other researchers often happens online anyway, as many are based in countries all around the world. Working remotely, i.e. from home, requires perhaps a bit of discipline and structure and I try to keep to a daytime routine.

What do you do when you are not working or teaching?

I have always been very interested in sports, particularly rugby, cricket and various martial arts. I loved playing rugby at high school, but in more recent years I have hugely enjoyed training and competing in jiu jitsu, a grappling/wrestling martial art and sport. I hold a purple belt and teach the beginners’ classes at my academy. There are interesting correlations between the pursuits of mathematics and jiu jitsu. In both cases, you have to accept some confusion and frustration and learn to persist until things become clearer.

Tell us a bit about your background?

I grew up in Johannesburg with my father, who has a background in accounting, tax and banking, and my mother who is a teacher. I went to school at St Stithians. I moved to Cape Town to study actuarial science and while I love the Mother City, I enjoy visiting Johannesburg and would say that I appreciate both cities for what each has to offer.

Q&A with Mansa Aidoo, AIFMRM’s new lecturer

Qualified quantitative analyst Mansa Aidoo dreamed of a career in academia. When an opportunity presented itself at her alma mater, the African Institute of Financial Markets and Risk Management (AIFMRM), she knew it was the right move for her.

You did the MCom in Risk Management of Financial Markets at the institute two years ago. How does it feel to be back?

Honestly, it’s great. I always wanted to work in the academic world, but I thought of going into industry first, making contacts and connections and gaining practical experience. I worked at Nedbank as a graduate quantitative analyst for a year and the experience was very rewarding. But when I thought about what I wanted to achieve and which direction my career should take, it was always academia that beckoned.

What in particular attracts you to the academic world?

I think it is the pursuit of knowledge and research coupled with interacting with students and making abstract theory simple to understand. Many students are struggling with complex fields of study, and I know what it is like because I went through all of this myself. I also helped tutor and counsel students when I was on campus and I understand the pressures many of them are under, especially those from underprivileged backgrounds. I would love to help more students, especially black students, excel in the various fields of finance.

Is there a particular message you have for students who are struggling?

Keep going, persevere and stay strong. There are many people to talk to at the university, and they will all tell you to keep going. I learnt that struggling is normal, and even failing at times is not the end of the world. But you must not let it define you. If there is a particular subject or field of study that troubles you, find someone to talk to or pursue some of the many online tools available. There is so much help for students, and often they only need to be made aware of the various channels. If you are still battling, decide if there is another way to achieve the outcome that you desire – or if you should be doing something else. The important thing is you have to keep learning and growing.

Tell us a bit about yourself, where you grew up and the journey that brought you to the AIFMRM.

I grew up in the Eastern Cape, in Mthatha. My mother died when I was very young, but I was surrounded by my three sisters, my father and later, my stepmother, so there was always a lot of support and guidance growing up. I enjoyed school and from a young age worked hard. My favourite subject was mathematics and I came to UCT to study actuarial science. However, I ended up switching to finance because it was more in line with where I envisioned myself at the time. By the time I did the MCom in Risk Management, I knew I had found the direction I wanted to pursue in my career.

You say you learned much about success from your family. Can you tell us more about that?

My father taught me about perseverance. He was a senior superintendent for the municipality’s electrical department, and his philosophy was that if one door closes, another always opens. I have carried this lesson with me, and it has helped me many times in life.

Do you have a specific field of interest in terms of research?

I am currently looking into a topic for my PhD thesis, possibly in the area of credit risk, which I think is very interesting from an African perspective and especially now. I believe the financial sector has an important role to play in the development of local economies, particularly as we rebuild the economy in the post-COVID-19 world. The financial sector will be able to contribute in valuable ways. Additionally, I am interested in how financial inclusivity and access to finance can be improved and I hope to contribute to the body of work in this area with new research of my own.

What do the pope, x-rays and the game of Go have in common?

By Professor Thomas McWalter and Professor Jörg Kienitz

Artificial Intelligence (AI) has been in the headlines recently, with the pope joining Microsoft and IBM in a quest to outline principles for powerful emerging technologies and their regulation. Broadly, this kind of AI refers to a wide range of technology, interdisciplinary approaches and applications including big data, cloud services and the use of machines capable of performing tasks that typically require human intelligence.

Advances in computer power, machine learning and predictive algorithms are creating paradigm shifts in many industries. For example, when an algorithm outperformed six radiologists in reading mammograms and accurately diagnosing breast cancer, this raised questions around the role of machine learning in medicine and whether it will replace, or enhance, the work being done by doctors.

Similarly, when Google’s AI software AlphaGo beat the world’s top Go master in what is described as humankind’s most complicated board game, The New York Times declared “it isn’t looking good for humanity” when an algorithm can outperform a human in a highly complex task.

Both these examples point to narrow uses of AI, specific types of machine learning that are hugely effective. The medical example illustrates supervised learning, where a computer is programmed to solve a particular problem by looking for patterns. It is given labelled data sets, in this case, x-rays with the diagnosis of presence or absence of breast cancer. When given a new x-ray, the computer applies an algorithm based on what it has learnt from all the previous x-rays to make a diagnosis. Unsupervised learning is a kind of self-optimisation where a computer has a set of rules, such as how to play Go, and through playing millions of games, it learns how to apply these rules and how to improve.

Machine learning is a phenomenal tool. To fully harness its potential it is essential to understand what machine learning is – and isn’t – and to demystify some of the hype and the fear around what it can and can’t be used for. We have anthropomorphised computers; we speak about them in terms of intelligence and learning. But in essence, a machine computes – it does not learn. Its algorithms are designed to mimic learning. In essence, these algorithms minimise the errors of a complicated function that maps inputs to outcomes and we interpret that as solving a problem, but the machine doesn’t know what problem it is solving or that it is playing a game. The intelligence rests with the humans who design the algorithms and configure them for specific tasks.

Now, more than ever, we need intelligent and very well-educated people who can apply these techniques in the correct context and interpret the results. When an algorithm fails, the consequences can be catastrophic. An obvious example is a fatal accident caused by a self-driving car. We need to build in fault tolerance. Data integrity is also an important issue – what we put in is going to affect what we get out. Education is critical in making sure we get these elements right. And of course, there are broader ethical issues to consider surrounding data collection such as what data can be used, where it is sourced, and whether different data sets can be combined.

Machine learning is particularly valuable in the financial sector. Many applications are already in use in banking, insurance and asset management. Financial institutions use pattern recognition very successfully for fraud detection. It is also valuable for looking at trends in data sets and finding patterns that humans may not be able to identify directly, for example, in profiling people who apply for credit. There are even robo-advisory applications for individual asset allocation. In financial modelling, machine learning can be applied to pricing, calibration and hedging.

For example, valuing derivatives contracts depends on many complex factors and variables such as interest rates, exchange rates, equity values – all of which fluctuate all the time. Financial mathematicians use models for this, but they are complicated and not easy to solve in a closed form. We may be able to build and apply a model to one contract, but banks have hundreds of contracts, and risk management and regulatory frameworks need to be updated all the time. Machine learning, specifically deep learning and neural nets provides a powerful shortcut. We can use classical numerical methods to produce financial models and then use them as labelled data sets – as in the x-ray example. An algorithm can take this input to generate the output for multiple contracts.

Industries and organisations that are pulling ahead are figuring out where to replace standard methods and complex, time-consuming computations with machine learning. They are also using it for more complex modelling approaches, adding further variables that cannot usually be factored into standard methodologies. The most obvious benefit is that it is faster and of course, machines can compute millions of times faster than humans. These techniques also have the potential to be far more accurate and allow us to make better-informed decisions.

But the human element is critical. The accuracy of potentially life-changing outcomes will depend on how we identify where we use these techniques, how we build the algorithms, how we choose and manage data and finally in how we interpret and act upon the results.

Professor Thomas McWalter is an applied mathematician who lectures computational finance on the MPhil in Mathematical Finance at the African Institute of Financial Markets and Risk Management (AIFMRM) at UCT.

Professor Jörg Kienitz lectures at the University of Wuppertal and is an Adjunct Associate Professor at UCT. His research interests include numerical methods in finance and machine learning applied to financial problems and derivative instruments.

AIFMRM hosted a machine learning for option pricing masterclass in Johannesburg on 5–6 March 2020, taught by Professor Jörg Kienitz and Dr Nikolai Nowaczyk.

Q&A with Dr Mesias Alfeus, AIFMRM’s new Postdoctoral Research Fellow

Dr Mesias Alfeus, who joined AIFMRM as a Postdoctoral Research Fellow, has had a remarkable and inspiring academic life-journey. After a childhood spent in a small village in northern Namibia, his studies brought him to South Africa and later took him to Sydney, Australia. Now back in Cape Town, Dr Alfeus is looking forward to continuing his research and making a contribution to closing Africa’s skills gap.

Welcome to AIFMRM, Dr Alfeus. Can you tell us a bit about your life story?

I was born in Namibia and raised by my great grandmother. I was a quiet child, but I was ambitious, and I wanted to be educated. It was challenging for me growing up in the poor village of Okadila, where people used to see me through the lens of my shortcomings. I was often absent from school because of the responsibility I had for herding livestock, and as a result, I failed the first semester of grade one. I remember I would carry my books with me while cattle herding. Sometimes the animals would get into people’s fields while I was studying and I would be beaten for allowing that to happen. But I continued with my schooling and was encouraged by my great grandmother and my teachers when I became keen on mathematics.

After high school, I was awarded a scholarship by the Ministry of Mines and Energy, so I moved to Windhoek to study a bachelor’s degree in pure and applied mathematics at the University of Namibia. Then I went on to my honours and master’s degrees in mathematical finance at Stellenbosch University.

I moved to Australia for my PhD in quantitative finance at the Business School of the University of Technology Sydney (UTS), under the supervision of Professor Erik Schlögl. My thesis was on the stochastic modelling of new phenomena in financial markets. After completing my PhD, I was fortunate to secure an academic position doing research on mathematical finance models and lecturing financial mathematics at the University of Wollongong, Australia.

What brings you back to Africa and AIFMRM?

AIFMRM is one of the most reputable institutions in Africa in mathematical finance, with excellent connections to industry, so this is a wonderful opportunity for me to continue my research. I’m happy to join this vibrant team and community that has an international track record in both teaching and research. It’s a thrilling experience to be in such a highly regarded institution and also part of a global community. In some ways, it was a sacrifice to leave my academic position in Australia, but it’s a sacrifice worth making as I’m happy to be back in Africa. Here I can figure out how to contribute to SADC countries in terms of the knowledge gap and try to discover something new in mathematics that will be useful for industry and have a real social impact. Also, my wife lives here in Cape Town, so that’s another drawcard!

What will the focus of your research be?

I’m continuing my PhD research into developing stochastic techniques and challenging the classical assumptions, for modelling new phenomena in financial markets. I feel this is important as market risk becomes increasingly complex in rapidly developing industries. Today’s mathematical theory and standard techniques for modelling risk may no longer be sufficient – we need to develop models that can be adjusted to market changes and new technology. So, I am continuing to deep dive into the classical theory of mathematics to do this.

I am a mathematician by training, but I was drawn to finance because I am eager to understand the usefulness of mathematics in finance. My motivation for undertaking a PhD, and now in continuing my research, is to become an independent and solid researcher and to be well-rounded and confident in developing mathematical models beyond classical assumptions. I feel my PhD training has given me the opportunity to come up with new cutting-edge research that’s useful to industry.

I’m hoping that my research can be a useful guide to policymakers or central banks in making complex decisions around how to control the interbank lending rate for the effectiveness of monetary systems. One nice feature of these classes of models that I’m working on is that one can numerically solve various systems of risks and be able to quantify the level of each component of risk. These models are developed to capture multi-dimensional sources of market risk at once – rather than one by one.

What are your hopes for your time at AIFMRM?

To make a solid contribution to solving real-world problems in finance. And I’m also looking forward to interacting with all students and staff and visiting academics – AIFMRM encourages a lot of academic collaboration. I’m aiming to build my career and my research reputation. Something else I am excited about is that I recently received an invitation from SpringerBriefs in Quantitative Finance series to write a book about my PhD topic, which is a great honour.

Ensuring graduates are adding value in an age of AI

By Professor David Taylor

The precise rate of AI adoption in South Africa is unknown. Some reports suggest half of the country’s larger businesses are actively plugging in, while others indicate that South African companies are slower on the uptake. Regardless, the transition to a workplace where AI has a significant role to play is underway and is having a knock-on effect on the skills required by business – especially at entry-level.

“During this transition, fewer positions will be available, and we will see a significant shift in skills requirements for entry-level positions,” commented World Wide Worx MD and 4IR project principal, Arthur Goldstuck, recently. “This, of course, is the fundamental challenge of the 4IR (fourth industrial revolution).”

This issue is playing out particularly vividly in the global financial services sector, which, according to a McKinsey report, is one of the leading adopters of AI and Machine Learning.

From banking to trading, AI is reducing the time it takes to generate reports, analyse risks and rewards, make decisions, and monitor financial health. AI is used to give more accurate, personalised advice, combat fraud, automate savings, make indecipherable data intelligible for service providers and their customers, and make self-help options viable, practical, and safe. These are many of the skills that financial services graduates are traditionally trained to do.

University graduates today are stepping into a world where they will be working alongside AI, and they will need a different skillset – and mindset – to do so. Research is identifying a “growth mindset” as a key requirement in workplaces where humans and computers work side by side. A term coined by Stanford University professor Carol Dweck, having a growth mindset means that you believe your talents can be developed (through hard work, effective strategies, and input from others). By contrast, if you have a fixed mindset, you believe your talents are innate gifts. Those with a growth mindset tend to perform better in the modern workplace because they “worry less about looking smart and put more energy into learning.” They don’t get as easily knocked back by criticism or failure because they are less defensive, quicker to admit error and move on, and more likely to share and collaborate.

The good news is that while developing a growth mindset is not easy (it seems that a fixed mindset is often the default setting for our brains) – it can be done. And it starts with helping individuals to become more self-aware.

“To remain in a growth zone, we must identify and work with [our] triggers,” says Dweck. “Many managers and executives have benefited from learning to recognize when their fixed-mindset “persona” shows up and what it says to make them feel threatened or defensive. Most importantly, over time, they have learned to talk back to it, persuading it to collaborate with them as they pursue challenging goals.”

It falls to educators – from primary school through to university – to make sure that we are preparing our students for the future world of work. It is our responsibility to develop not only the technical skills and competencies they need but also the self-awareness and associated mindsets that will make them more resilient and adaptive. Furthermore, we will have to cooperate more closely with industry recruiters to understand their precise needs with regards to talent. This is the premise upon which the African Institute of Financial Markets and Risk Management (AIFMRM) at the University of Cape Town was founded more than five years ago.

Against a backdrop of skills shortages, 8.3% of graduates are reportedly struggling to find jobs in the current economy; this suggests that academic institutions and the world of work and business are somewhat misaligned. Realignment is essential for the survival of academia, industry and the economy. And in the age of AI, this challenge is magnified.

The World Economic Forum estimates that automation will displace 75 million jobs worldwide by 2022, but that with sufficient economic growth, innovation, and investment – especially in wise human capital development – there can be enough new job creation to offset the impact of automation.

AI can’t do everything. It (currently) cannot make moral decisions or explain how it came up with a particular solution. It is essentially subordinate to its algorithm and generally doesn’t act in ways that are outside of its ‘training’. It is these functions that graduates will need to excel.

We must embrace this imperative and act together systemically to find the best of traditional education and re-wire it to emerging requirements and trends in the workplace so that graduates can complement the role of AI and maximise its benefits for consumers and the economy.