How Python is Used in Finance and Fintech – Netguru

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In this blog post
Jakub Protasiewicz
Common in applications that range from risk management to cryptocurrencies, Python has become one of the most popular programming languages for Fintech Companies.
Its simplicity and robust modeling capabilities make it an excellent financial analysis tool for researchers, analysts, and traders.
Python has been used with success by companies like Stripe, Robinhood or Zopa.
According to the HackerRank 2023 Developer Skills Report, the Python programming language was among the second most popular languages.
eFinancialCareers showed that during the last two years the number of finance-related jobs mentioning Python has almost tripled, growing from 270 to more than 800. Organisations like Citigroup now offer Python coding classes to banking analysts and traders as a part of their continuing education program.
“We’re moving more quickly into this world” – Lee Waite, the CEO of Citigroup Holdings CEO, said in an interview. “At least an understanding of coding seems to be valuable”.
Python continues to remain one of the most demanded programming languages in the bank industry – eFinancialCareers reports.
Read on to find out more about how finance organizations and fintechs are using Python to create cutting-edge solutions that impact the entire financial services sector.
Several features of Python make it a great pick for finance and fintech. Here are the most significant ones:
Python is easy to write and deploy, making it a perfect candidate for handling financial services applications that most of the time are incredibly complex.
Python’s syntax is simple and boosts the development speed, helping organizations to quickly build the software they need or bring new products to market.
At the same time, it reduces the potential error rate which is critical when developing products for a heavily-regulated industry like finance.

The financial services sector needs to be more agile and responsive to customer demands, offering personalized experiences and extra services that add value. That’s why finance organizations and fintechs need a technology which is flexible and scalable – and that’s exactly what Python offers. Using Python in combination with frameworks such as Django, developers can quickly get an idea off the ground and create a solid MVP to enable finding a product/market fit quickly.
After validating the MVP, businesses can easily change parts of the code or add new ones to create a flawless product.
One example of successfully following the MVP approach could be the Clearminds platform which was developed using Python and Django. Now they offer financial advice and investment tools.

Languages such as Matlab or R are less widespread among economists who most often use Python to make their calculations. That why’s Python rules the finance scene with its simplicity and practicality in creating algorithms and formulas – it’s just much easier to integrate the work of economists into Python-based platforms.
Tools like scipy, numpy or matplotlib allow one to perform sophisticated financial calculations and display the results in a very approachable manner.
In the dynamic landscape of the finance industry, Python emerges as a versatile ally, seamlessly integrating with cutting-edge technologies to streamline development processes and enhance overall efficiency. One of the key strengths of Python lies in its ability to eliminate the need for developers to build tools from the ground up, resulting in substantial time and cost savings for organizations.
Notably, Python plays a pivotal role in bridging the gap between finance and emerging technologies such as blockchain, cloud computing, and big data. The finance industry, with its complex data structures and intricate risk management systems, benefits immensely from Python’s adaptability and expansive ecosystem.
Python’s prowess extends beyond its core capabilities, as it becomes a linchpin in data analysis within the finance sector. Leveraging robust Python data analysis libraries, developers can process and interpret vast datasets, contributing to the creation of sophisticated risk management systems. The language’s simplicity and flexibility make it an ideal choice for crafting intricate financial models and analytical tools.
Furthermore, as fintech products increasingly require seamless integrations with third-party services, Python serves as a facilitator. Its extensive set of libraries streamlines integration processes, allowing organizations to effortlessly connect with external services. A notable example is the straightforward integration with Truelayer, providing access to OpenBanking APIs, or with industry giants like Stripe.
The marriage of Python with finance extends beyond traditional realms, finding application in advanced risk management systems. By harnessing Python’s capabilities, organizations can develop robust systems that analyze intricate financial data, assess risks, and respond dynamically to market fluctuations.
Python’s integration capabilities play a pivotal role in fortifying the finance industry against the challenges of the modern era. From revolutionizing data analysis to seamlessly connecting with external services, Python stands as a cornerstone in the development of innovative solutions that empower financial organizations to adapt and thrive in an ever-evolving landscape.

Python is surrounded by a vibrant community of passionate developers who contribute to open-source projects, build practical tools, and organize countless events to share knowledge about the best practices of the Python application development. There is the Python Weekly newsletter or the PySlackers Slack channel. For official community information, one can visit the Python.org community section. Not to mention sites dedicated to learning Python and sharing Python knowledge like RealPython or DjangoGirls which also have their own communities.
If it comes to open-source projects, almost every Python framework is maintained by the open source community – it’s possible to help with the development of Django, Flask, OpenCV and many more.
Python is evolving as a programming language and gaining more popularity every year. All that makes it easier to source and hire talented Python developers who add value to fintech or finance projects. Organizations that invest in solutions made with Python can be sure that their technology is stable and not going to become obsolete anytime soon.
Python comes in handy for financial professionals in a broad range of applications. Here are the most popular uses of the language in the financial services industry.
Python is widely used in quantitative finance – solutions that process and analyze data from large datasets, big financial data. Libraries such as Pandas simplify the process of data visualization and allow carrying out sophisticated statistical calculations.
Thanks to libraries such as Scikit or PyBrain, Python-based solutions are equipped with powerful machine learning algorithms that enable predictive analytics which are very valuable to all financial services providers.
Examples of such products: Iwoca, Holvi.

Finance organizations build payment solutions and online banking platforms with Python as well. Venmo is an excellent example of a mobile banking platform that has grown into a full-fledged social network.
Thanks to its simplicity and flexibility, Python comes in handy for developing ATM software that enhances payment processing.
Examples of such products: Venmo, Stripe, Zopa, Affirm, Robinhood

Every business that sells cryptocurrency needs tools for carrying out cryptocurrency market data analysis to get insights and predictions.
The Python data science ecosystem called Anaconda helps developers to retrieve cryptocurrency pricing and analyze it or visualize financial data. That’s why most web applications that deal with cryptocurrency analysis take advantage of Python.
Examples of such products: Dash, enigma, ZeroNet, koinim, crypto-signal
Stock markets generate massive amounts of finance data that require a lot of data analysis tools. And that’s where Python helps as well. Developers can use it to create solutions that identify the best stock trading strategies and offer actionable, predictive analytical insights into the condition of specific markets. Use cases include algorithmic trading in fintech products,
Examples of such products: Quantopian, Quantconnect, Zipline, Backtrader, IBPy

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Jakub Protasiewicz
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