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The exponential growth in data is driving companies to look at how they can use new insights to their competitive advantage. Managing and analysing this increasing volume of data, using strategies such as AI and machine learning, has opened up significant skill gaps in the financial services sector. Which is where digital learning has a big role to play.
Today’s competitive advantage comes from using data intelligently.
One of the most significant commercial developments being talked about today is how accessible technology has become; tasks that once relied on expensive super-computers can now be done from a small hand-held device from anywhere. Combine this with ubiquitous data on pretty much anything you can think of, and the way that businesses seek to gain competitive advantage is being rapidly reshaped.
If we go back twenty years, Goldman Sachs employed around 600 cash equity traders from its New York offices. Today however, the company reports to have just two. These cash equities traders, who needed little in the way of technology know-how, have now been replaced by automated programs. However, it is rarely just a case of machine replacing human, because maintaining the trading programs still requires a large (albeit reduced in numbers) team of people. The key point is that this new team has an entirely different set of skills to the original traders.
This scenario is familiar throughout the financial services sector. Yet despite this, the traditional education route – one in which finance professionals would use a finance degree or similar at university – is leading to a shortage in the appropriate skills to support the shift. Today, finance professionals need to be able to do other things such as modelling or coding to have the skills that can generate alpha .
This skills gap is not unique to a specific generation and in fact there are two broad categories of people:
- Existing finance professionals in their mid to late career that need to upskill to keep up with change and new opportunity
- Younger people entering the industry who need skills from day one, and who are probably not receiving these skills when they graduate from universities
How digital learning is helping bridge the skills gap
One hot topic in the investment industry is portfolio optimisation through “quantamental” investing – where more diversified data is used to blend quantitative and fundamental research. Investment companies are directing resource to enable them to add alpha by analysing an increasing volume and variety of datasets. Yet traditional or existing talent pools don’t necessarily offer the skill sets required to do this.
Ongoing development of our own digital learning program, the Certificate in Quantitative Finance (CQF), highlights the significant pace of change impacting the gap. The program has gone through three main evolutions since it was launched back in 2003. First was the pre-Global Financial Crisis phase, where structured products were the main focus. This was followed by a post-Global Financial Crisis phase driven mostly by the regulatory response to the crisis. Now we are in this new third phase, whereby course content has shifted to focus on utilising data to generate alpha.
The rapid growth of data and the use of AI and machine learning has fuelled the skills gap even further. It is said for example, that approximately 90% of the data that exists today was only created in the last two years. Thus, an educational curriculum has to be constantly updated to stay relevant to new trends and focuses such as sustainable investing. With this in mind, we review our CQF syllabus every three months so that whatever a participant learns on a Tuesday night, they’ll be able to apply in their work on a Wednesday morning.
The shifting demographic of people enrolling on the CQF course is also indicative of the demand for new skills. When the course launched, the demographic was quite narrow. Today however, the demographic is far more diverse and we enrol traders and fund managers through to IT professionals, consultants and those working in regulatory-driven areas like model valuation. Plus, there is a significant increase in students from India and China, where finance professionals are looking to upskill to compete with their counterparts in more developed financial centers.
In conclusion, while AI and machine learning strategies might seem new, they are a response to increased and varied data sets. Adding alpha, by effectively analyzing and understanding all this data to recognise opportunity, calls for different skills. These are skills which many education institutions, such as universities, are finding it hard to keep up with. This is why digital learning will be key in plugging the skills gap around quantitative finance, and furnishing individuals and companies alike, with the skills that will provide them with a competitive edge.
