Python has become a common programming language in applications that range from risk management to cryptocurrencies used by Fintech companies. The simple yet rich modeling capabilities of Python has made it a staple for researchers, analysts, and traders in the field.
Well-known companies like Stripe, Robinhood and Zopa all use Python successfully for various needs.
According to a report by HackerRank from 2018, Python was one of the top three most popular programming languages in financial services. Today, Python still seems to be one of the most prominent languages in the banking industry.
What makes Python so compatible for fintech and finance projects?
1) Python is simple and flexible
Python is easy to learn and write, making it a great tool for handling financial services applications which are most often very complex. Due to its simply syntax, Python has a faster development speed, which helps organizations build and deliver software more quickly to market. Furthermore, Python reduces the potential error rate due to its simplicity, a critical factor when developing products for a heavily regulated industry like finance.
2) Python allows you to build an MVP quickly
An important determinant of success in financial services is to be more agile and responsive to customer demands. As such, offering personalized experience and extra services are beneficial to this goal. With Python, companies can create a solid MVP to enable a product or market fit quickly. After, they can quickly and easily change parts of the code to create a flawless experience.
3) Python is a bridge between economics and data science
It is much easier to integrate economic work into Python-based platforms as opposed to languages such as Matlab or R. This is again a result of Python’s simplicity and practicality in creating and running algorithms and formulas.
4) Python has a wide variety of resources
With Python, developers no longer need to build their tools from scratch, instead, they can benefit from the rich ecosystem of libraries and tools that Python provides. Specifically for finance, fintech products often require third-party integrations, which are easy to achieve with Python.
5) Python is the fastest growing, most popular programming language
Python’s popularity allows it to have a passionate and engaged community of developers who continually contribute to its growth, building practical tools and organizing countless events to share knowledge and discuss best practices. As a result, by using Python in finance and fintech projects, organizations benefit from the pool of talented Python develops who are continually striving to make Python better.
How is Python used in finance?
There are several ways Python is currently being utilized in the finance and fintech industries.
1) Analytical tools
Python is commonly used in quantitative finance, creating solutions that process and analyze big datasets and financial data. Thanks to the rich libraries available to Python users, Python-based solutions come with powerful machine learning algorithms that allow predictive analytics which are incredibly valuable to all financial services providers.
2) Banking software
Thanks to its simplicity and flexibility, Python is useful for building payment solutions and online banking platforms, like the popular mobile banking platform Venmo used in the United States.
Businesses that sell cryptocurrency need tools for carrying out cryptocurrency market analysis and get insights and predictions. Certain Python ecosystems are especially useful for developers when retrieving cryptocurrency pricing and performing analysis.
4) Creating trading strategies with Python
A successful trading strategy relies on correct and in depth market analysis. That’s where Python comes in. Developers can use Python to create solutions that identify the best trading strategies as well as give predictive analytical insights into the condition of specific markets.
So why is Python a good choice for fintech and finance?
With its ease of use, simplicity and flexibility, Python provides the correct level of attention to detail when dealing with complex datasets and analysis that is required in the finance industry. The rich ecosystem of libraries and tools makes Python a great choice for developers who need to analyze and offer predictions on large sets of data and complex problems, as they can rely on Python’s existing resources instead of creating something from scratch. Moreover, Python is a still growing language, meaning that these benefits will only improve in the future as Python’s popularity and use increases. For all these reasons, Python is a good choice for fintech and finance.