Top 4 Programming Languages for Fintech

by James · February 23, 2023

Fintech as an industry has always been very receptive to new technologies. The huge volume of transactions, the low tolerance for risk and the need for immediate processing made software a perfect force multiplier and the most important competitive advantage for Fintech companies.

While choosing a Fintech software, companies consider many criteria such as price, scalability and possibility of API integrations, reliable high performance, compliance, security, time to market, etc. But all these indicators are primarily influenced by the programming language used.

Each programming language has its advantages and disadvantages. Some of them can increase efficiency, flexibility and security. Legacy backend technologies become less valuable and can expose Fintech software to operational and security risks. In this article, we will discuss the top 5 programming languages for Fintech software development and describe the pros and cons of each.

1. Java

Java is an object-oriented language known for its portability, scalability and enhanced security features. Java is used in many Fintech systems because it is an established language with an extensive library of third-party components, making it easy to develop complex Fintech software.

Java has robust and easy-to-use security APIs and uses OOP concepts such as encapsulation, abstraction, and inheritance, which increase security and prevent unauthorized user access.

The programming language provides high stability and can operate on all devices.

Let’s look at each of the disadvantages that Java has separately.

Slow and poor performance. Compared to some other programming languages, Java requires more memory and is relatively slower due to the extra work required by the interpreter to convert code into machine language. The Java Virtual Machine performs various backend functions, including automatic garbage collection, which can reduce application speed and affect performance.

Poor graphical user interface. The graphical user interface builder in Java is poor and cannot build complex user interface.

Cost. Due to higher processing and memory requirements, Java is slightly more expensive than other options.

2. Python

Given its popularity worldwide, it is no surprise that Python is significantly influencing the Fintech industry. Google searches tracked by PyPI indicate that Python ranks as the most popular programming language globally.

Python has gained popularity because it is simple, flexible and one of the easiest coding languages to learn – especially for beginners. Python’s clear and easy-to-read syntax is essential for writing programs and applications that address complex economic challenges. It can also be scaled to meet the needs of different financial companies, from small startups to large banks.

The extensive collection of tools and packages in Python’s library simplifies the life of programmers by eliminating the need to build projects from scratch, saving valuable time and effort. Python is popular in data analysis, machine learning and AI.

Python doesn’t have many disadvantages, but there are some limitations you should be aware of.

Speed limit. Python is a dynamically typed and interpreted programming language, and therefore the program is not fast when running code. This can be a problem if speed is a critical point in the project.

Limitations for mobile development. Python performs poorly when used to create mobile applications due to slow program processing speed and below average memory efficiency.

Security issues. Python’s database access layer is relatively underdeveloped with certain limitations when accessing databases. Therefore, the programming language is vulnerable to security issues, and its use comes with inherent risks that must be kept in mind.

3. Golang

Golang, also known as Go – an open source programming language developed by Google in 2009 – is quickly taking over other programming languages for Fintech companies that want to get to market quickly and need a technological backend that is simple, scalable and easy to maintain.

According to HackerRank, Go stands out as one of the fastest growing languages. Go’s popularity is growing at a rate of 190% and employer demand for Go increased by 301% (as of December 2022).

Go has many attributes over other programming languages. is designed to handle high-load services, making it an ideal choice for building Fintech applications. It’s simple and compiles at ninja speed. At the same time, it allows multiple applications to operate simultaneously, resulting in better performance and use of resources. Cross-platform compatibility is a crucial functionality in Fintech applications; one that Golang offers. It allows devices or software to operate across more than one hardware platform or operating system. Thus, Fintechs can gain access to a much larger audience by offering access to applications across different platforms or devices, including tablets, smartphones and laptops.

Fintech companies such as Paypal, Nubank, Velmie, Chime, Capital One and American Express already use Go as their programming language because it is so well-suited to running high-load, distributed applications built on a microservices architectural foundation.

One of the main disadvantages of Go is its relative newness to the market, which means it may have fewer libraries and resources available compared to more established languages. In addition, it can be challenging to find experienced developers who are proficient in Go.

4. Ruby

Ruby is known for its simplicity and productivity, which is why many startups in the digital finance industry use it alongside the Ruby on Rails framework. The built-in security features of the framework and the ease of use of the language, which saves time and money, are some of the main reasons why developers prefer it. Ruby is also known for its security and flexibility, which makes it suitable for building various financial products, including payment systems and dashboards, and is a strong choice for creating an MVP (minimum viable product).