Finance as an industry has always been very receptive to new technologies. The sheer volume of transactions, the low tolerance for risk and the need for instant processing made computing technology and the internet a perfect force multiplier for banks.
But all of that technology has to be developed. Finance has been at the forefront of that, if not directly then by partnering with the major IT firms. If you look at the earnings reports of most IT companies, you will find that banks are some of their largest clients. Banks’ fascination with IT has reached a point where they are directly investing into a lot of these IT companies and fintech start-ups alike. Some even have in-house teams working on blockchain and other bleeding edge tech.
All of this stuff is rather interesting, but you need programming skills to get the job done. I learned C++ and a bit of Java as part of my Computer Science degree over a decade ago. But things have changed so much since then.
What are the new languages that you need for finance and fintech? For developing fintech apps, for financial modelling, for running simulations, for data science, for developing AI trading algorithms?
Python has definitely taken the finance world by storm. Python does have an elegance about it in the way things are handled. Its learning curve is not as steep as some of the other languages. It is a high-level language which makes it more accessible to researchers and quants and that accessibility has certainly added to its appeal.
Python is especially popular for machine learning, data science and AI applications. These are certainly some of the bleeding edge applications in finance and fintech which is why Python finds such favour in our industry. It also lends itself quite well for mathematical applications like statistics thanks to its large array of libraries. No points for guessing how important that is in finance.
The immense demand for Python in the financial services industry and an acute shortage of talented Python programmers gives it the top spot in this list. Here are some Python Courses that have been chosen based on their conformity with the requirements of the financial services industry:
Learning C++ back in college was one of the most memorable experiences of my life. But I swear I am not going to let that bias influence its ranking here!
The beauty of C++ is that it is closer to the machine as compared to most of the other languages on this list. That means it is much faster which makes it ideal for High Frequency Trading systems. HFT requires such low latency that firms pay tens of thousands of dollars for the privilege of placing their servers right inside the stock exchanges!
Another advantage of C++ is that most legacy bank systems were built using C++. I would certainly be a rich man if I had a nickel for every time our IT guys used the phrase “Legacy System”.
The finance tech world is still dominated by C++ programmers but the only reason I haven’t placed it higher on this list is because there are already plenty of good C++ programmers out there. Nevertheless, it is still a solid choice especially if its speed that you need.
Excel and VBA are ubiquitous in finance. Bankers and traders use VBA as their daily drivers, and it is often built into some of our systems. Macros are used for handling data, models may be created using VBA for risk management, investment strategies, scenario testing, valuations and so on.
VBA is essentially a force multiplier and time saver. Rather than spending time each day duking it out with those spreadsheets, you can just simplify your work using macros. There is generally no excuse for not knowing Excel and VBA and you would only be making your own life easier by learning it.
Java is used extensively in the financial services industry. Some of the world’s largest banks use it for their electronic trading platforms, retail and corporate banking portals, wealth management offerings and other such front-end, customer facing applications.
Java’s popularity in the finance industry might have something to do with its enhanced security and cross platform capabilities. Security is intrinsically important in finance and the ability to offer your platform to users on a variety of platforms is also a core requirement.
Choose Java if you want to create front-end applications for banks or fintech firms.
MATLAB is favoured by applied mathematicians and that is how it found its way into finance. The quants need it for the stuff they do in finance. It is useful for floating point linear algebra and it is useful for generating plots and other such interactive tasks in finance. In fact, it is rather famous for its legendary plotting tools.
It is also a very fast language in terms of time to code which makes it attractive for traders or structurers who need to test things out in a fast-moving market.
R is also used heavily in data applications and statistics. If you are interested in a data analyst or data scientist type role in finance, R might actually be your top choice.
It is not the easiest of languages, but it is good at what it does – data. Whether that be statistical computing or data visualisations, R is probably your best bet.
While the steeper learning curve might turn off some users, those who stick around are rewarded. In the long run, you would probably be better suited for data applications than those with other language skills. It also means less competition when looking for jobs with your specific skillset.
SQL is not a full-fledged programming language but nevertheless it is something that you would probably need to learn to handle the copious amount of data that our industry produces.
SQL is indispensable in handling structured data where you need to maintain the relationships between multiple variables. Business and financial analysts use SQL to find patterns and turn mountains of data into useful information.