Flexibility is power. To build secure and reliable financial technology (FinTech) software, you need to compose a technology stack that is easy and comfortable to work with. At the same time, your technology choices must account for key financial industry requirements: security, performance, and customization.
With Python, a high-level multipurpose programming language that comes with thousands of standard and third-party libraries, you can meet all these requirements. In this article, we discuss how Python is used in financial software development.
We talk about how using Python can help tackle core tasks a FinTech solution is expected to handle and what libraries and frameworks you can use for specific tasks. We also discuss four major aspects to take into account when you choose Python for building a FinTech solution.
This article will be useful for project leaders and development teams who are choosing their FinTech software technology stack.
Specifics of building FinTech software
The FinTech sector covers operations ranging from banking transactions to stock trading forecasts to blockchain payments. By 2030, the global FinTech market is expected to reach over $930 billion, compared to just $155 billion in 2021. To meet the needs of this rapidly growing market, FinTech businesses and organizations spend a lot of time and money delivering competitive software solutions to their customers.
Choosing the right programming language is the starting point for composing your perfect technology stack. Some products are created using a single language, while others are built through a combination of several programming languages. Your choice of technology stack will depend on many factors, including the following:
1. Compliance with strict requirements. Businesses and organizations working on FinTech solutions must comply with global, local, and industry-specific requirements, including:
- General Data Protection Regulation (GDPR)
- Payment Card Industry Data Security Standard (PCI DSS)
- Gramm–Leach–Bliley Act (GLBA)
Aside from implementing core requirements of relevant regulations, standards, and laws into your solution, it’s important to keep track of the latest compliance requirements and make timely changes to your product.
2. Top-notch data security. FinTech solutions process tons of sensitive personal and financial data. As a result, the financial industry has some of the strictest requirements for data and application security.
You need to make sure that you can follow common security best practices, like the ones suggested by National Institute of Standards and Technology (NIST) and Open Web Application Security Project (OWASP), and provide your users with advanced data protection capabilities. For example, you can implement more sophisticated security features such as advanced encryption or authentication. Running extra rounds of security and penetration testing is also an efficient way to improve your application’s security.
3. Service flexibility. Knowing and understanding the needs of your target audience is key to delivering a competitive solution. You might need to test several business ideas before releasing a product that can win over your customers. And even after a successful release, it’s vital to continue adjusting and expanding the product’s feature set.
To quickly deliver the first results and adjust your solution’s functionality on the go, you need to keep your solution flexible. The technology stack you use should allow for frequent updates without harming the software’s performance, security, and user experience.
4. High demand for innovations. For handling thousands of transactions and analyzing millions of records, modern FinTech solutions rely heavily on technologies like big data, the blockchain, biometrics, machine learning, and artificial intelligence. Yet incorporating such technologies into your software requires niche knowledge and rare expertise.
One way to power up your development team with this expertise is by engaging an outsourcing development team that has experience working with the technologies you’re interested in. Depending on the scale of your project, you can delegate the development and testing of separate features or the whole product to an external team.
All these challenges as well as specifics of particular projects affect the choice of programming language for FinTech software. Languages commonly used for developing FinTech applications include C#/C++, Ruby, Java, Golang, and Python. So why should you choose Python for your FinTech solution? You’ll find out in the next section.
What makes Python a good choice for building a FinTech solution?
Is Python good for finance? In short, yes.
There are several reasons to consider adding Python to the technology stack of your FinTech project. Let’s look at the most meaningful:
1. Fewer critical errors
With the financial industry being one of the most heavily regulated, delivering a quality and scalable solution is crucial. In particular, the industry dictates strict requirements for how user and application data should be handled.
With Python, you can significantly reduce the rate of critical errors in your solution thanks to the language’s simple syntax, which resembles plain English. This enables developers to quickly detect potential errors during code reviews — if not avoid making critical errors in the first place. Along with coding guidelines like PEP 8, Python offers a wide selection of standard and third-party code review tools meant to help developers check the readability and maintainability of their code as well as to detect bugs and high-risk vulnerabilities.
2. Fast delivery of first results
Enabling a significant increase in development speed, Python allows your development team to reduce the overall time to market for your software.
Python works well for quickly building prototypes and minimum viable products (MVPs). Starting with a basic functionality set, you can evaluate your business idea, check how customers respond to your offer, and find investors for your Python FinTech project.
This language also provides the flexibility needed for timely adjustments, which is critical for FinTech solutions. Thanks to Python’s performant code, you can easily change existing functionality as well as add new features to your software while maintaining the required level of performance and security.
3. Wide selection of third-party libraries
With hundreds of thousands of libraries and packages to choose from, Python offers a rich selection of solutions for building quality FinTech products. And most of the tools and libraries are open-source.
You can easily find a library for running complex calculations, incorporating machine learning models, making data-driven predictions, or integrating your product with a third-party service. In the following two sections, you can learn about some of the most popular Python libraries and packages and the tasks you can use them for.
4. Data analytics capabilities
Another reason to choose Python for FinTech solutions is that this language works great for all sorts of mathematical tasks and allows for smooth integration with data science tools. Using this language, you can easily create formulas and algorithms for performing sophisticated financial calculations.
Python also supports integration with MATLAB, which is a platform commonly used by financial professionals.
5. Cross-platform availability
Last but not least, when using Python for FinTech enterprise solutions, you can easily make your product available across multiple platforms. Delivering cross-platform software is a sure way to reach a broader audience, attracting new customers and improving the user experience for existing ones.
Using Python for finance applications, you can build a product that supports the following platforms:
You can also use Python to program single-board computers like Raspberry Pi.
Main use cases for Python in FinTech
There are multiple ways you can use Python in the banking industry: from traditional banking software to cryptocurrency exchange platforms. However, most FinTech tasks that can be tackled with the help of Python fall into one of three categories:
Let’s look at each of these use cases.
1. Payment processing
Payment solutions are one of the most common use cases for Python in finance. This language comes with secure APIs, great scalability, and smooth payment gateway integration — all crucial for delivering a competitive FinTech solution.
Organizations operating in the financial industry use Python to build ATM software, digital wallets, transaction management platforms, and cryptocurrency solutions.
Commonly used tools: To build websites or APIs for payment processing systems with Python, developers often use the Django framework.
- Venmo — a social network–like mobile banking platform offering a wide selection of money transfer services
- Affirm — a financial service working under the “buy now, pay later” principle
- Robinhood — a stock investment and trading application
2. Analytics and predictions
Trading organizations, banking institutions, and digital markets all need to process and analyze large volumes of financial data. FinTech players use Python to build solutions for developing stock trading strategies, predicting changes in cryptocurrency prices, approving loans, and so on.
Using dedicated Python libraries, you can deliver an application that can analyze large datasets, detect patterns in processed data, extract valuable insights from the processed data, and help you predict possible outcomes.
- Holvi — a FinTech platform providing banking, invoicing, and bookkeeping services
- QuantConnect — an algorithmic trading platform for quantitative research and data analysis
3. Data visualization
Being able to process and analyze large volumes of financial data is crucial for FinTech solutions. However, to make the results of such analysis useful and actionable, financial applications require additional data visualization capabilities.
Data visualization turns the results of complex calculations into easy-to-understand charts, graphs, and diagrams. By visualizing the results of data analysis, you can make it easier for end users to spot patterns in data, determine possible trends, and make efficient data-driven decisions.
- Anaconda is a data science ecosystem for cryptocurrency pricing analytics that, among other features, offers rich data visualization capabilities
When using Python for FinTech development, you get access to a rich selection of libraries and frameworks that can help you efficiently tackle almost any task. However, there also are several significant considerations to take into account.
4 things to consider when building a Python FinTech solution
Finally, let’s talk about some technical aspects you should pay close attention to when working on a Python application for the FinTech domain. Depending on the tasks and purposes you have in mind for your product, the list of critical features may vary. The following four requirements, however, are common for all solutions written in Python:
1. Ensure high application availability
Before you start building new Python software, you need to plan its deployment with high availability in mind. A modern solution is expected to effectively handle potential server failures, avoid downtime and outages, and automatically restore lost data if needed.
To make sure that your application is highly available, you can:
- Deploy the application over multiple servers
- Distribute the servers across geographical zones
- Ensure database availability with various scaling methods
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2. Keep critical data well-secured
FinTech solutions continually process different kinds of sensitive data, from customers’ personal data to transaction details. Being a valuable asset for cybercriminals, such data requires enhanced protection.
Here’s what you can do to improve the security of the data processed by your FinTech software:
1. Scan your software for vulnerabilities
Make sure to thoroughly analyze your product for potential security breaches and vulnerabilities. Python offers a large selection of dedicated security analysis tools. You can start with commonly used static analysis tools like bandit or Dlint.
Also, make sure to sanitize external data and remove any sensitive information from external sources that your application isn’t supposed to handle. This can be done with the help of the schema and bleach libraries.
2. Encrypt critical data
Encryption is a great way of securing sensitive data from the risk of compromise. This approach enables you to ensure privacy and secure storage of business-critical data and efficiently protect such data from unauthorized access. Also, make sure to enable real-time notifications so you can start remediating detected security threats as soon as possible.
While the standard Python library doesn’t have built-in encryption functionality, you can encrypt data processed by your software with the help of external libraries such as PyCryptodome, PyNaCl, and Cryptography.
3. Deploy advanced authentication methods
Another great way to enhance data protection and reduce the risk of accidental data disclosure on the end user’s side is by deploying advanced authentication methods. In particular, you may consider adding capabilities like multi-factor authentication (MFA) and biometrics-based authentication to your FinTech application.
For implementing MFA, you can use PyOTP — a popular Python library for generating and verifying one-time passwords. For biometrics authentication, specifically for methods based on fingerprint or face recognition, you can rely on machine learning libraries like Keras and OpenCV.
Note: If poorly planned or executed, additional security measures may prevent end users from comfortably working with your product or service. Therefore, you need to double-check how a specific security measure affects the user experience before moving forward with its implementation.
3. Document your code properly
Having properly composed code documentation will be beneficial for both developers and end users working with your Python software. Documentation should be detailed and well-structured, explicitly covering the functionality of your code and how to use it. This means that once you generate project documentation, you need to make sure to review and update it on a regular basis.
Composing all documentation manually is possible, but it usually takes too much time, especially within large projects. Instead, you can turn to dedicated tools that generate documentation automatically. Commonly used tools for generating code documentation include Epydoc, Read the Docs, FastAPI, and Sphinx.
4. Optimize application performance
Flawless performance is one of the key features of successful and popular software. Performance testing is a proven way to assess the current state of your application and determine what needs to be improved. By analyzing the results of performance tests, you can evaluate:
- Performance under load
- And more
It’s vital to run performance tests regularly and use their results to efficiently optimize your solution’s performance. JMeter and Locust are two common tools you can use to execute load and performance testing of a Python application.
Python provides businesses operating in the financial sector with an opportunity to build quality and secure products faster than with other popular programming languages. The flexibility of Python code makes it easy to change existing software features and enhance your software with new functionality so you can efficiently meet your end users’ needs.
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