Last Updated: September 29, 2021
· anna11

Why Python Is The Best Programming Language For Fintech Software

Why is Python a more beneficial language for FinTech?
Let me tell you what Python is. This is a rather old but one of the most popular programming languages in the world. The development of Python started at the end of the 80s of the last century, and the first full-fledged version was released in 1991. As time goes by, this language is gaining momentum. Today Python is prevailing in FinTech software development due to a few good reasons you will find below.
According to the survey conducted by HackerRank among 20+ US-based companies involved in FinTech industry, you can see on the chart that Python language is a preferable for FinTech industry.
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eFinancialCareers (employment website from the Wall Street Journal) added Python to six best programming languages for the banking industry. Moreover, today this language is the most-taught in technical universities.
Python language has a very quick runtime, that is making the language a preferable option for FinTech area. Some functions can be implemented much faster using specifically Python. As an example, one function in Python can take about 10 strings of code, whereas C++ will require twice as many strings.

Why Cleveroad prefers using Python to other languages in FinTech
Each area, whether it be healthcare or social media, requires an individual approach and the selection of the proper development tools. Considering the large experience in the development of various software, Cleveroad programmers can safely say that Python could beat such giant competitors like Java, SQL, and C++ in the financial area for today.

Working with algorithms
To understand why it is the most appropriate language for FinTech, you should pay some attention to such technical challenge like an algorithmic problem and why Python helps our developers solve it easily.

The algorithmic problem can be the first trouble developers face during the development of any FinTech app. Since FinTech software is tightly connected with many figures, calculations and so on, the software should be very smart to work with a large number of math tasks. That is why it is highly important to choose the right programming language. And Python shows its apparent advantages since its syntax is the closest to math syntax that is applied in financial algorithms.

In the process of development, it can be necessary to assign value parameters, and Python syntax makes it possible to do it quite fast.
Considering all the above, I would like to draw your attention to some more technical particularities our company singles out in Python during FinTech software development.

When it comes to dealing with math equations or any other task, Python allows our developers to convert any mathematical or algorithmic statement to simply one line of Python code.

For example, you intend to order the development of FinTech software that will require math function to help your customers optimize different financial processes. To implement it, Cleveroad developers use vectorization features that can be achieved with NumPy - Python open-source library for math calculations and support of high-level math function. It will allow you to create 50,000 calculations within one code line and speed up the development process and quality of your FinTech app.

Here it is worth noting that Python comes along with a large number of auxiliary libraries that enhance the process of interaction with math tasks. The task of our developers is to choose the right Python library for using it in the financial app development to provide you with a high-performance FinTech software. Using the right tool, FinTech app in Python becomes much faster than its counterparts written in other languages.

Quick compilation
And last but not the least advantage lies in the quick code compilation of the software being created. And some of corresponding Python libraries like Cython or Numba provide our developers with crucial functions that help compile Python code into machine code statically or dynamically. The processing speed becomes much faster and your software development is moving easier.