Why Python Is A Perfect Choice For Building Fintech Products?

Fintech is an ever-growing industry. Its revenue reached $121,5 billion in 2020 and the adoption rate for money transfer and payment and insurance technology among consumers reached 75% and 48% respectively. Fintech has numerous monetization models and it’s the industry that benefits both consumers, finance companies, and tech developers. Thus, it is unlikely that its development will slow down anytime soon.

Python is one of the most used technologies in the fintech industry. It is powering big products like PayPal, Stripe, Square, and others. Discover what Python is, why it is used in fintech, what types of industry products you can create with it, and examples of successful solutions powered by this language. Keep reading and discover everything there is to know about Python in fintech. 

What is Python?

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. It is a multi-purpose solution, meaning that you can use it for a variety of software. However, its most common use case is for working with data. 

Currently, it is the third most popular programming language, after JavaScript and HTML/CSS. It is mostly because it is beginner-friendly, easy to use, and fit for many purposes. 

What Are the Main Technological Challenges for the Fintech Industry and Products?

The fintech industry has numerous technological challenges that Python can help with. It includes the following issues:

Security Problem

One of the main reasons why people are hesitant to move their finances online is the concerns about software security. Cybercrimes take place in the fintech industry quite often since hackers can immediately receive a monetary reward by either clearing out the user’s bank account and digital wallet or selling data on black markets. Some of the attacks cost millions of dollars. For example, Mt. Gox, a digital cryptocurrency wallet, has to go corrupt because hackers managed to steal over $700 million in cryptocurrency. This slows down the adoption of financial technologies significantly. 

With Python, it is very easy to create authorization levels and implement encryption. There are a lot of resources on how to make Python fintech solutions more secure which eases the process of securing even more. 

Integration With Other Tools

Fintech solutions oftentimes integrate with other services to create the best customer experience and bring many solutions under one roof, rather than build disparate systems. For example, budgeting apps need integrations with banking, insurance tech needs integration with numerous integrations with different insurance providers, etc. Sometimes it is quite difficult to connect everything in a secure, unproblematic way. 

All the APIs needed for fintech integrations are working and easily available. It is partly because economists work mainly with Python too and it is easier to integrate a Python solution into the Python solution. 

Algorithmic Problem

Fintech solutions normally handle numerous different mathematical operations at once and it is a challenge to build software that handles that many operations, different in nature. 

Python is probably the winner over all other languages here. The language has long become the most used tool among mathematicians and data scientists for its quick assignment of value parameters, numerous libraries for data and math, and ease of use. Their visualizations tools are top-notch as well and they can process big chunks of data in no time. 

Easiness of Learning and Simplicity of Syntax

A lot of people start to learn Python because it is so easy. Does it mean that you can not create the big complicated products with it? No! Quite on the contrary, such an easy language allows fintech developers to think more about the complicated structure of the software rather than the complicated nature of the language. It is also easier to collaborate with Python since pretty much everyone can understand what you have written. 

The simplicity and shorter length of code (compared to other data languages like R) also guarantee a faster time-to-market which can significantly reduce the costs of your project. The speed is especially important for startups who need to create an MVP quickly and, consequently, get the funding faster. 

Working with Django

Python and Django work perfectly well together to ensure quickness of coding and availability of tools for working with data. 

With the Django framework, you do not have to write all the code from scratch. There are many reusable chunks for authorization and management tools, for example. It allows Python developers to quickly get over the simple tasks and concentrate more on essential and complex parts of your future software. 

Availability of Many Libraries

Libraries are yet another tool that helps you to easily solve popular problems, pull task-specific code and documentation. Python has numerous libraries, many of them specifically for economists, data scientists, and mathematicians. It is a perfect fit for finance, as you see. 

Some of the most popular Python libraries for the fintech industry include:

• NumPy: used for scientific computing, data science, and statistics. It has a multidimensional container for generic data, additional linear algebra, Fourier transform, and random number capabilities.
• Pandas: used for data analysis manipulation capabilities
• Pyalgotrade: used for algorithmic trading. It is especially useful for paper-trading, live-trading, predictive analytics in trading and stockbroking
• FinmarketPy: used for backtesting of trading strategies and analyzing financial market data
• SciPy: used for integrating AI and machine learning in fintech products
• PyFolio: used for analyzing performance and risk of financial portfolios

There are also less well-known libraries for more specific functions in fintech like quantecon.py, ffn, pypi, etc. 

Developers have plenty of resources to make their work easier and faster, and business owners can enjoy faster product delivery.

Huge Talent Pool

Python is loved by 67,83% of developers. It is easy and general-purpose, meaning that developers get to work with different projects and not get bored as easily. Consequently, most developers want to start learning it and there is already a huge talent pool to choose from. If you are a startup, you will surely find a dev team in no time and spend little resources on it. 

Great AI Machine Learning Capabilities

Python offers numerous tools for data science, AI, and machine learning which greatly help fintech. The language is able to process huge amounts of data and offers an extensive toolbar for that. Since machine learning is used in fintech for automated trading, fraud detection, personal finance, decision-making, and risk management, you might want to discover that option with Python. 

Analytics Tools

Analytics software is used for personal finance management where users track their income and spendings, plan budgets, etc. However, it is also used by big finance companies for analyzing the market, predicting the trends in the stock market, tracking their KPIs, etc. 

We have mentioned a couple of libraries that help to build analytics solutions. Python also offers good data visualization tools for easily digestible reports for users who are far away from finance. 

Digital Wallets and Payments

Digital wallets are on the rise, with online shopping and quick money transfers being in favor. Python is a preferred option for facilitating online payments and creating digital wallets due to its secure APIs and payment gateway integration. 

Banking Software

With Python, banking software becomes cheaper and faster to develop, easier to scale, and available for interconnected transaction management. Banking software is a must for a progressive, trustworthy, and client-oriented bank nowadays and Python makes it easy to create one. 

Cryptocurrency

Cryptocurrencies are an always hot trend that seems to stay, with governments starting to accept them. Python is a great choice for this fintech software type since it provides libraries for blockchain development and data science tools for predictive analysis and the best pricing. You can also build a tool for traders to analyze the market with Python’s libraries. 

Insurance

Insurance technology really benefits from AI and machine learning: it settles claims faster, detects fraud, performs risk management, etc. AI and ML are revolving around Python and that is how the language became a hit on the insurance market as well. 

Great Examples of Fintech Products Built with Python

Yes, it all sounds good on paper. Are there real use cases of Python in fintech? The answer is yes, plenty! Make sure to visit the upcoming FinTech conferences and events in 2022 to learn about the most successful Python use cases in this industry. But for now, let’s take a look at great fintech products that have Python in their technology stack. 

Robinhood is a trading and investing platform without commissions. It is an amazing tool for both beginners (there are no minimum account balances for those who want to just take a look) and experienced traders with its powerful analytics. The platform offers free learning resources as well, teaching basics about trading and investment and sending notifications with the latest news and trends on the market. Robinhood also works with cryptocurrencies, namely Bitcoin, Ethereum, and Dogecoin. 

The app of Robinhood was created with the help of Python and Django and they still recruit Python developers for newer projects. 

Venmo is a platform for transactions and social media all at once. Users can link their bank accounts, debit and credit cards, or issue a Venmo card to use the service. The social part comes in handy when you have to split bills and cabs with your friends and family, rent with your roommate, etc. You can request payment or send one. The payments usually come with funny text messages and emojis which later appear in your feed. You can also pay for other services with your Venmo account. 

Python is the dominant technology in Venmo’s tech stack and it has not failed the app yet. It handles 65 million users at the moment. 

Affirm is a representation of big trend BNPL or “Buy now, pay later”. Basically, a user chooses to Affirm at checkout, chooses their payment schedule, and pays in the following month at the pace that they are comfortable with. Unlike with credit cards, you only pay for what you need, you see the total with interest amount upfront, and there are no late, penalty, or annual fees, or compound interest. Affirm has become the choice of many because of its transparency and ease of use. 

In this unicorn, Python is used by data scientists for machine learning. 

Stripe is an instrument for online payments. It allows small online retailers to add payment checkout for credit and debit cards to their websites without any coding skills. Bigger companies need the help of a developer to integrate more powerful payment processing tools. At the moment, the functionality grew to invoice for freelancers and independent contractors, and offline terminals.  

Python was used in its development process, alongside Ruby on Rails. 

Summary 

As far as you see from this article, Python is a perfect choice for building fintech products as it helps to overcome numerous industry-specific challenges connected with security, integration, and algorithm creation. The technology has simple syntax, works with Django, offers numerous libraries, a huge talent pool, and great machine learning capabilities. 

Are you ready to utilize all the power of Python to create an award-winning product? If you are, then feel free to contact Ideamotive. We have extensive experience creating Python software for fintech and would be glad to profoundly analyze your case and offer the best solution to your needs.

If you are not sure if Python is the right technology for your product, reach out - our software consultants will analyze your needs and propose the perfect tech stack for you.