Banking and financial sectors are increasingly becoming tech-driven. The tech-driven financial industry gave birth to a different industry called Fintech. Thanks to the large impact of the web, mobile apps, and a host of other technologies made on the industry’s customer experience and growth, Fintech has now become the face of the entire financial industry.
Now let us provide some hard facts. According to a HackerRank survey conducted a few years ago, it has been found that while other industries have preferences for different programming languages, Python leads as the most loved programming language for the financial and banking industry. Though we have passed that survey by a few years, those findings have hardly been altered, and Python is now used by most leading brands in the financial industry.
How Python Becomes Such an Invariable Match for the Fintech Industry?
Why has Python become such a massive and unmistakable match for the digital solutions of the entire finance sector? Well, the reasons are many. Python is robust and sturdy enough to withstand the tremendous performance demands common to finance apps. Python is highly scalable to the growth prospects of financial companies and flexible enough to accommodate various feature needs typical to the sector. Lastly, Python uniquely allows creating all those features for computation and calculation typical to fintech apps.
Let’s have a look at these benefits one by one.
Faster Development and Time to Market
Thanks to the Python/Django stack, a finance app can be built quickly, allowing the businesses to reach out to customers early and gain a competitive lead. When you want to build a minimum featured MVP app for a bank or finance company, Python comes as the ideal technology choice. Thanks to this framework, developers don’t need to build all those user admin or authorization tools from scratch. This and many other key features can be built quickly by Python simply because the language offers an extensive collection of libraries.
You also get an admin panel and CRM solution built-in and ready to use for the MVP app. You only need to set this up for the new MVP app. Most importantly, when the MVP app is built and released, you can test the app and change some code to address any shortcomings.
Python for Complex Calculations and Computations
The economic and financial calculations and complex computations are part and parcel of any bank and finance company. This is another area where Python proves to be a highly effective framework. Python is preferred by data scientists worldwide simply because of its awesome abilities to accommodate calculations and complex computational tasks.
Extensive Range of Python Libraries for Banking Features
One of the greatest advantages of using Python for banking and financial applications, e.g. python trading bots, is that it comes well equipped with an extensive range of libraries that can be used to build all sorts of app and software features typical to the financial industry and banking. From transaction processing to complex computational tasks, for every finance-specific feature, you can find several helpful libraries.
The developers of fintech apps using Python only need to select the right library for the appropriate feature and function. It has been seen that banking and financial apps built with Python offer much-improved performance than apps built using other languages or frameworks.
Let’s have a quick look at some of the notable Python libraries used by most fintech apps.
- SciPy is a library for complex finance-specific and technical computing
- NumPy is another robust package for scientific calculations
- Pandas is a flexible and robust data analysis library
- Pyalgotrade is a library for creating trading algorithms
- Pyrisk is the library for handling financial risk factors and evaluating performance
- Zipline is another library for creating trading algorithms
- Quantecon.py is a great library for quantitative economic analysis and reporting
- Pyfolio is the library for risk and portfolio analysis
- Scikit-learn is the library for building machine learning algorithms
- FFN is a library for financial functions
- Pynance is the library for data visualization, data analytics, and data retrieval
Simple and Easy Syntax
Among the most notable reasons for using Python for fintech products, simplicity, and ease of use top the list. Python is great for fintech apps simply due to its straightforward approach and easy-to-understand syntax. Because of this, Python’s learning curve is really low, and one can learn coding in Python quickly. The highly readable code that looks like plain English in many ways helps developers create the app quickly and bring changes faster.
The most notable thing is that the Python code is easy to understand, not just for the experts with technical abilities but also for the financial companies. This allows facilitating easier collaboration between the clients and the developers when Python is used in a project. A Python project can easily establish an understanding between the clients and the developers.
World’s Biggest Financial Brands Use Python
Now that we have described all the key advantages of using Python for fintech apps, it is also important to provide some credible examples as the real-life proof of the Python’s popularity and acceptance in the fintech sector.
As of now, the language has been used by hundreds of leading fintech brands all over the globe. In emerging economies, Python has become the most popular language for building fintech apps of all sizes and financial niches. Fintech app developers in India are finding Python ideal for competitive budget development with a clear focus on optimum output and faster time to market.
Python is the principal language that J.P. Morgan used for building its Athena program. Similarly, Bank of America used Python for building its Quartz program. According to the insiders, at both companies, there are thousands of developers who know Python and can implement the language effectively in different contexts.
Citigroup, as of 2018 as one of the leading investment banks, made it mandatory for the analysts and traders to possess expert Python coding skills. From July last year, the company for its newly hired analysts made it mandatory to complete Python training. The investment banker is even going further to add Python skills and expertise to the skill set of top managers and experts in the company.
Python will continue to remain as the most dominant programming language for the banking and financial software solutions and fintech apps. If you want to develop a unique fintech solution, it is ideal to start up-skill your expertise by learning Python.