Best Programming Languages for Finance & FinTech in 2021

The fintech industry has grown to exciting heights in the past decade, expanding to produce everything from efficient mobile banking apps to blockchain-encrypted data storage. As a result, more and more career opportunities are emerging for aspiring fintech professionals.

A 2019 PwC survey of more than 500 companies found that 80 percent of companies in telecommunications and 75 percent of companies in financial services are creating fintech jobs. However, 42 percent of those companies are “struggling to fill these roles,” PwC writes.

How can you take advantage of this demand and create an exciting new career? One way to stand out to employers is to learn the best coding languages for finance. Programming skills are vital to creating forecasting models, developing trading algorithms, building new apps and tools for customers, and much more.

But where do you start? This article will introduce some leading programming languages for fintech, discussing their various uses and characteristics, along with helpful resources for learning more.

1. Python
2. Java
3. C++

4. C#
5. Ruby
6. SQL

1. Python

Python is influential in fintech, which isn’t surprising, as it ranks as the most popular programming language in the world, according to Google searches cataloged by PyPI.

HackerRank notes that Python is the second-most-requested language that employers reference in interviews. Further, Python ranks first among fintech interviews and third in finance, according to the HackerRank survey.

Python is a popular fintech language because it’s simple, flexible, and one of the easiest coding languages to learn — especially for beginners. Professionals use the language in a variety of industries and, as a result, more than 51 percent of hiring managers look for candidates who know Python, according to HackerRank.

What’s more, Python’s syntax is comparatively clear and easy to read — an important point when writing programs and applications to address complex financial issues. Python can also be scaled to meet the needs of many different financial companies, from small startups to global banking and trading firms.

Python’s vast library of tools and packages makes life much easier for programmers, saving them the time and effort of building projects from scratch. Python’s functionality and range of resources have made it useful in data science, machine learning, and AI, which are driving the key technologies in financial services. These qualities also make Python one of the best programming languages for quantitative finance.

As you learn more about programming languages, take note of how often Python appears on educational curricula. For instance, Columbia Engineering Coding, Data Analytics, and FinTech Boot Camps all include Python in their courses of study. These bootcamps can offer a great introduction to the most-valued coding languages in business today.

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2. Java

Java is the top-ranked programming language in finance, according to HackerRank, for reasons that mirror its general cross-industry popularity. The language has a friendly learning curve, can handle significant amounts of data, and boasts rigid security features. These characteristics have contributed to Java’s popularity in banking and finance for more than 25 years — and because Java has been used for so long, it remains a key language of choice even as new languages emerge.

Java is a general-purpose language, meaning it can be used to write programs and apps for a variety of purposes. Due to its versatility (Java shows up on websites, mobile devices, and internet-enabled appliances), Java has helped revolutionize how we shop and bank. Programmers use the language to build e-commerce platforms, banking apps, and trading algorithms in quantitative finance.

Java’s continued popularity also stems from its “write once, run anywhere” platform. That means, programs written in Java can run on any machine. Portability is important in finance, where programmers, companies, and consumers all might be using different devices and operating systems.

To summarize Java’s staying power, Red Hat Software Senior Solutions Architect Leon Matthews writes, “There is a reason that Java has stood the test of time and continues to be so widely used in banks — in addition to being considered one of the more secure programming languages, it is also one of the most resilient, and one of the foundational programming languages for innovations in the banking industry.”

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3. C++

C++ is another veteran programming language, with roots in a late 1970s college thesis project. Bjarne Stroustrup sought to add object-oriented programming to C without changing the language’s speed or functionality. The result was C++.

C++ is considered very fast, in part because it is a compiled language, meaning that a compiler translates its code to machine language. Since the computer requires less translation to understand the code you’re writing, it operates more efficiently.

Many companies, including financial institutions, have been using C++ for years to develop software, operating systems, and other products. That longevity has embedded C++ in many industries, including those in finance and fintech. It’s also commonly used in quantitative finance. In addition, developers have built thousands of libraries and tools to complement its use. And despite its age, C++ is a favorite language for machine learning and AI, again because of its speed.

Now, C++ does have a steeper learning curve — the site cplusplus.com says the language can seem “a bit more cryptic” than others based on its usage of special characters. But C++ has shown its staying power in finance and appears to be here to stay.

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4. C#

C# (pronounced “see sharp”) is a next-generation version of C++ that also maintains a strong foothold in fintech (the fourth-ranked language) and finance (fifth-ranked), according to HackerRank. C# is an object-oriented language used to build dynamic applications that run in the Microsoft .NET ecosystem. Like C++, C# traces its roots to the C family of languages, and programmers who know C++ and Java will find it familiar. Therefore, C# is another handy programming language for those in finance and fintech.

C# is an important language for companies that build applications on Microsoft’s .NET development framework. The language was created specifically for the framework, making app-builds easy and secure. It also benefits from a huge user community and library of tools. Many developers recommend Microsoft’s .NET as the framework of choice for fintech, thus making C# a core component.

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5. Ruby

The makers of Ruby call it a language that focuses on “simplicity and productivity.” Perhaps that’s why >companies — especially startups — in the world of digital finance use Ruby in conjunction with its Ruby on Rails framework. Developers cite ease of use (which saves time and money) and the framework’s built-in security features as their top reasons for their preference.

Ruby is a free, open-source language that launched in 1995. Its creator, Yukihiro “Matz” Matsumoto, said he sought to develop a language that “is simple in appearance but is very complex inside, just like our human body.” Ruby’s popularity grew with the development of the Ruby on Rails framework, which is used widely in developing web applications.

Ruby shares many programming traits important to fintech — notably speed, security, and flexibility. Programmers use Ruby to build many kinds of financial products, including payments systems and dashboards. The Rails framework helps simplify the code-writing process and further speeds up the product-creation process. For startups looking to create their first MVP (minimum viable product), Ruby is considered a strong language choice.

Additional Resources

6. SQL

SQL is different from other programming languages mentioned here. You won’t build websites or applications using it. But SQL is vital to finance and fintech because it harnesses the power of databases, making it an essential tool for those working in finance.

SQL (commonly pronounced “sequel”) is one of the primary languages used for communicating with databases. Where Python, Java, and others are general-purpose programming languages, SQL is considered to be domain-specific. It works primarily with relational databases to store, locate, retrieve, and manipulate data within and from them.

Financial institutions generate enormous amounts of data that they need to analyze. And professionals who work in business, marketing, sales, and finance understand the importance of proper data analysis to their success. SQL is the conduit to that success. It is part of data processing platforms, used in statistical modeling, and is a burgeoning skill among financial analysts.

Little wonder, then, that professional developers made SQL their third-most used language, according to a 2020 Stack Overflow survey. In short, if you want to become a better financial analyst, consider learning SQL.

A bar graph that displays the most commonly used programming languages used by developers.

How to Learn Programming for Finance and FinTech

Ready to acquire some new programming skills to begin, advance, or shift to a fintech career? If so, you probably have questions, such as where to get started. Learning to code is a process that people can approach in different ways: they can study programming in college, explore learning independently, or enroll in a bootcamp for a concentrated, collaborative course that provides a targeted approach.

The following are a few leading methods for learning finance- and fintech-oriented programming skills:

Bootcamps

Bootcamps are short-term, intense educational courses, often conducted online, that focus on the in-demand technical skills that today’s employers demand. Their curricula, usually conducted over 3-6 months, can target coding, data analytics, fintech, and other technical disciplines, offering robust learning opportunities and practical experience.

To study programming for finance and fintech, you might want to consider these three bootcamp options. Each offers a unique path that might appeal to you, depending on the career course you’re charting:

Columbia Engineering Coding Boot Camp

This bootcamp focuses on the key skills required for a career in full stack web development. Browser-based technologies such as JavaScript, HTML, and CSS are course essentials and the foundation of web development.

The course also goes beyond full stack development, covering some of the key skills of finance and fintech. Working with databases is part of the curriculum’s technical phase, and a free continuation phase addresses Python, Java, C#, and Amazon Web Services (AWS). The bootcamp provides these courses through asynchronous content that is available for one year after bootcamp completion. These no-cost courses are project-based, giving learners the ability to continue their professional development and add to their portfolios. This bootcamp can be a good choice for learners, no matter their coding experience, who are considering web development and might want to apply those skills to a career in finance or fintech.

Columbia Engineering Data Analytics Boot Camp

The data analytics bootcamp prepares learners for careers as data and business analysts or as entry-level data scientists. These positions are critical in the financial industry.

This part-time, 24-week bootcamp includes many of the necessary fintech skills we’ve covered. Python is a major focus, as are SQL and machine learning. It also dives into fundamental statistics, including modeling and forecasting.

Data professionals who work in business might consider this bootcamp as a way to advance their careers. Though no coding experience is required, the bootcamp recommends that applicants hold a Bachelor’s degree or have at least two years of experience in finance, business, statistics, or a related field.

Columbia Engineering FinTech Boot Camp

This bootcamp spotlights financial technology and analysis during a 24-week course, whose second module will be of particular interest to fintech learners. During this financial programming session, participants learn Python, SQL, financial modeling, and statistical programming. The module also covers Application Programming Interfaces (APIs) and conducting financial analyses with them.

Like the data analytics bootcamp, the fintech bootcamp does not require coding experience but does recommend a bachelor’s degree or at least two years of professional experience in finance, statistics, or a related field.

Traditional Degrees

College remains a go-to path for those entering finance, particularly learners just beginning their education. And quantitative analysis is a popular major for learners who want to merge their passions for finance and technology.

Quantitative analysts (or “Quants”) put their mathematical, statistical, and programming skills to use for banks, investment firms, wealth managers, and insurance companies. They can pursue degrees in economics or finance but also might consider engineering, computer science, or other degrees that emphasize mathematical training. Eighty-five percent of quantitative analysts have a college degree, according to CareerOneStop, and 37 percent hold a Master’s degree or higher.

College degrees continue to carry value in tech fields, with nearly 62 percent of developers holding a degree in computer science, computer engineering, or software engineering, according to Stack Overflow. And how popular is Python in college computer science departments? This list of worldwide schools that teach Python underscores the language’s demand on campus.

A bar graph that displays the most common college degrees held by developers.

Independent Learning Options

Looking to learn solo? You’re not alone. About 16 percent of developers in the 2020 Stack Overflow survey favored forgoing a formal education.

Books, videos, and online tutorials abound for those who want to learn Python, Java, C++, or any programming language valuable to finance and fintech. Python and Java, for instance, offer resources to help learners get started. Several educational communities provide self-paced, low- or no-cost learning opportunities that coding newcomers or career transitioners can apply to several financial fields. And professional sites such as LinkedIn have built courses covering Python and other financial programming skills.

Independent learning can work for motivated self-starters who relish a challenge and know what they want to learn. For those who need more structure or a broader approach, college might be the better option. Meanwhile, learners with the drive to succeed and desire to focus on the essential programming skills for finance could consider a bootcamp. The choice is yours, so get programming.

For more information, check out Columbia Engineering Boot Camps.

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