How Python is Used in Finance and Fintech (2024)

How Python is Used in Finance and Fintech (1)

Jakub Protasiewicz

Updated Feb 27, 2024 • 11 min read

How Python is Used in Finance and Fintech (2)

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.

What makes Python such a great technology for fintech and finance projects?

Several features of Python make it a great pick for finance and fintech. Here are the most significant ones:

It's simple and flexible

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.

How Python is Used in Finance and Fintech (3)

It allows building an MVP quickly

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.

How Python is Used in Finance and Fintech (4)

It bridges economics and data science

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.

It has a rich ecosystem of libraries and tools

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.

How Python is Used in Finance and Fintech (5)

It's popular

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 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.

Using Python in finance

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.

Analytics tools

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.

How Python is Used in Finance and Fintech (6)

Banking software

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

How Python is Used in Finance and Fintech (7)


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

Building a stock trading strategy with Python

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

How Python is Used in Finance and Fintech (8)

Wrap up: Python, an optimal technology for finance

The financial industry is a challenging one. Organizations that want to compete on the market need to develop products that are secure, functional, and fully compliant with state and international regulations.

  • Attention to detail is critical as well because these solutions almost always include integrations with financial institutions, services, and bank API connections that need to run smoothly.
  • Python's clear programming syntax and amazing ecosystem of tools make it one of the best technologies to handle the development process of any financial service.
  • The HackerRank 2023 Developer Skills Report indicates that Python is the second language developers are going to learn next. That means Python's ecosystem will continue to grow, offering organisations access to an increasing number of experts who will integrate the language further into the area of financial services and fintech.
How Python is Used in Finance and Fintech (2024)


How Python is Used in Finance and Fintech? ›

There are also many uses for this language in the world of fintech. Python was successfully used to build digital payment solutions (Stripe), financial analytics software (Kensho), banking platforms (Revolut), as well as cryptocurrency and stock marketplaces (Robinhood).

What is the use of Python in FinTech? ›

In terms of technologies, Python is one of the most popular programming languages for fintech development. It's widely used for analytics tools, banking software, and cryptocurrency because of its data visualization libraries, data science environment, and wide collection of tools and ecosystems.

How is Python used in the financial industry? ›

How is Python used in finance? Python is mostly used for quantitative and qualitative analysis for asset price trends and predictions. It also lends itself well to automating workflows across different data sources.

What coding is used in FinTech? ›

C++ is a common choice for Fintech companies that value speed. Companies that engage in online trading of stocks or other economic assets might choose to work with C++ because this language helps create low-latency programs.

Is Python a good skill for finance? ›

Launch or Advance Your Career

That's because Python is one of the most popular programming languages in finance and finance technology. Programmers use Python to build banking apps, enable economic forecasts, gather and analyze large quantities of financial data, and more.

Which Python library is used for finance? ›

NumPy. The mathematical library, like Pandas, fundamentally centers around logical figuring and has some expertise in cluster activities. NumPy bundle accompanies a wide assortment of mathematical capabilities, making it a significant library in the scholarly world and money industry.

Why do accountants use Python? ›

Python is unbeatable when it comes to automating tasks. Python can automate the tedious parts of accounting and make it easier for accountants to process more clients' work than ever before.

Is Python the future of finance? ›

Python is the best programming language to use when developing scalable and safe online banking systems. This dynamically typed language can be used to create online applications as well as payment gateways, stock market trading platforms, financial planning software, ATM software, and more.

Is Python useful in banking? ›

Also, Python works well in the financial industry because it can handle mathematical computations. Python's use in finance is anticipated to grow due to the banking sector's and other quasi-financial institutions' growing need for technological cooperation.

Which banks use Python? ›

Yes, many banks and financial institutions use Python/Flask for their software solutions. Some of these include Bank of America, JPMorgan Chase, Wells Fargo, and Citigroup.

Is Python a FinTech? ›

There are also many uses for this language in the world of fintech. Python was successfully used to build digital payment solutions (Stripe), financial analytics software (Kensho), banking platforms (Revolut), as well as cryptocurrency and stock marketplaces (Robinhood).

Is Zelle a FinTech? ›

Who Owns Zelle? Zelle is a product of Early Warning Services, LLC, a fintech company owned by seven of America's largest banks: Bank of America, Truist, Capital One, JPMorgan Chase, PNC Bank, U.S. Bank and Wells Fargo.

Do financial analysts use Python? ›

Analysts use Python to make stock market predictions and create machine learning technologies related to stock.

Which Python is best for finance? ›

In summary, here are 10 of our most popular python courses
  • Machine Learning for Trading: New York Institute of Finance.
  • Using Machine Learning in Trading and Finance: New York Institute of Finance.
  • Google Project Management:: Google.
  • Introduction to Finance and Accounting: University of Pennsylvania.

What is an example of Python in finance? ›

One of the main ways that financial professionals use Python for financial modeling is to build models that forecast financial performance based on historical data. For example, a financial model might be used to forecast the future earnings or cash flows of a company based on its historical financial data.

How much Python is required for finance? ›

Python for finance requires skills and knowledge that go beyond Python basics. This means that learning the finance and fintech uses for Python requires a thorough understanding of Python principles. An instructor can help you build a solid understanding of basic and advanced Python skills.

Does FinTech need programming? ›

Can you get into FinTech without programming knowledge? Yes indeed. You can build a successful career in FinTech without programming or coding knowledge. Even if you are a non-tech professional, having programming knowledge is not crucial to start and lead FinTech projects.

How is Python being used now in big tech companies? ›

You can use Python to build web apps, APIs, machine learning models, or software applications. And if you're interested in data analysis, you've probably seen that Python is one of the primary programming languages for data scientists.

What is the role of Python in blockchain? ›

The extensive library ecosystem of Python plays a significant role in revolutionizing blockchain development. These libraries provide a wide range of tools that greatly facilitate and expedite the development process, making Python a preferred choice among developers in the blockchain community.


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