Common in applications that range from risk management to cryptocurrencies, Python has become one of the most popular programming languages among financial institutions and fintechs. Its simplicity and robust modeling capabilities make it an excellent tool for researchers, analysts, and traders. According to the HackerRank 2018 Developer Skills Report, Python is among the top three most popular languages in financial services. 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. Organizations like Citigroup now offer Python coding classes to banking analysts and traders as a part of their continuing education program. 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.
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.
After validating the MVP, businesses can easily change parts of the code or add new ones to create a flawless product.
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.
It has a rich ecosystem of libraries and tools
With Python, developers don’t need to build their tools from scratch, saving organizations a lot of time and money on development projects. Moreover, fintech products usually require integrations with third parties, and Python libraries make that easier as well. Python’s development speed enhanced with its collection of tools and libraries builds a competitive advantage for organizations that aim to address the changing consumer needs by releasing products quickly.
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 Python development. Python is evolving as a 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.
Here’s how finance companies use Python today
Python comes in handy 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 solutions that process and analyze large datasets. 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.
