Innovative and revolutionized digital technologies are playing a vital part in the financial landscape. They provide splendid opportunities to make tedious paper-based processes more efficient, increase self-service options for customers and offer a unique opportunity for financial organizations, such as banks, brokerage, equity trading, and investment companies while improving customer services. Recent survey states Python has established itself as a leading choice to develop financial software. Let’s consider what key features make Python the best possible fit for creating software solutions for the financial sector.
Driving factors to use Python when developing a financial software
Python is great for fintech and can be applied to resolve challenges raised by the financial landscape when looking at analytics, compliance, regulation, and volume of data, etc. Let’s discover key factors why companies should focus on Python to benefit:
- Open-sourced. Created under OSI-approved flexible and open license that makes it ready-to-go to offer a wide range of libraries and toolsets for finance.
- Versatile. Used for fast, interactive coding as well as for building low-level systems, large applications, high-level analytics tasks, etc. especially in finance.
- Cross-platform. Applied to build a desktop including web applications, utilized on the clusters, servers, or small devices, available for the widely used operating systems (Windows/Linux/Mac OS).
- Friendly data layout. Has built-in list and dictionary data structures which can be used to construct fast runtime data structures; offers the option of dynamic high-level data typing which reduces the length of support required code.
- The optimal time to market and high performance. Provides enhanced process control, has a clean object-oriented design, maintains strong integration and text processing functionalities, offers own unit testing infrastructure increasing the time to market and productivity.
- The steep learning curve and a huge community. Offers excellent code readability, and uncluttered simple syntax to be easily learned. An active community of software engineers provides huge support while development.
Spheres of Python programming language implementation in the finance
When using Python for finance, we usually find essential its packages applied to a wide range of financial areas. Let’s dive into details below:
Digitalized solutions for banks
Innovative technologies are now revolutionizing the banking system. As a result, the banking landscape is going to shift in the coming years. There is a wide range of transformation set to take place in terms of technology in banking. Python technology provides splendid opportunities for banking organizations to change the ways they are operating by making the banks more digital and providing easy access without the necessity of going to the banks physically. It will also help to increase the products and services that banks are currently offering and help the bank to expand their customer base by developing customized solutions to serve every customer’s needs. Also, with Python, banking organizations can offer frictionless customer-business digital banking, instant or one-click payments between consumers, and provide locational services and offers to customers. As an example, Global banks have already seized the opportunity of Python, implementing it directly into their business. J.P. Morgan uses it for their programme (Athena), while Bank of America Merrill Lynch has built their integrated trading platform Quartz with Python being such a highly sought in the banking sector based on its productivity and simplicity.
Although the cryptocurrency market is relatively new, it has experienced significant volatility due to vast amounts of interest. It has seen a huge jump in terms of growth and advancement. Since the first cryptocurrency creation, the digital currency market has changed drastically. Some virtual coins have disappeared, while others have evolved with astonishing rapidity. For example, Bitcoin dominance is nearly 53% now, with over $112 million U.S dollars total market value. Due to the popularization of bitcoin and other digital coins, more and more businesses started to adopt innovation to grow and boost revenue. However, determining the value of cryptocurrencies is a bit tricky and worthwhile to know how to analyze, predict in advance some changes, etc. With Python, Django, pyCrypto, Anaconda, and other toolsets, it is not a problem anymore. If successfully implemented, they help to analyze, identify or forecast the factors such as cash flow or available assets to prosper in the cryptomarket.
Online trading is gaining traction nowadays: the practice of buying and selling of financial assets to profit has widely utilized recently. Business organizations of all sizes understand the importance of trading online and are interested in developing such solutions. Most of the leading financial institutions and stock brokers provide platforms and tools for easy trading online. With Python, you can get the best possible price at minimal costs and without significantly affecting the stock price and make rapid split-second trading decisions fast. If you are thinking of trading solutions, Python technology is excellent to create them. Better ROI, faster transactions, real-time monitoring are not all benefits you can get if properly developed.
Data analysis in finance
A vast number of financial organizations are revealing new ways to integrate digital solutions into their processes to maximize output. With terabytes of data assets, many businesses are focusing on enhancing their services and moving more users through the sales funnel faster. A lot of organizations have already automated repetitive, time-consuming operations such as analyzing, monitoring and evaluating. With Python, systematically applying statistical and/or logical operations is usually done to illustrate, convert, recap, or evaluate data. If developed and integrated correctly, it helps financial organizations track, analyze, share and make data-driven decisions. Also, they can measure various metrics such as individual performance, team spirit, morale, and collaborations to streamline workflow processes and help employees spend more time on higher-level tasks.
Companies that include Python in their tech stack
Here we have presented the most popular fintech companies applying Python technologies. Let’s take a closer look below:
- Stripe. Potentially great solution for online-only businesses that helps to accept and manage online transactions. It allows e-commerce or web-based businesses to receive payments through their websites. Being plugged into websites and apps, it connects with a credit card, banking systems/platforms in real time, allowing them to receive payments. At its core is a robust payments engine that streamlines the money flow in your business while connecting with applications that enable you to prevent frauds, manage revenue, and drive global business growth by means Ruby, Scala, Python tech stack.
- Zopa. Being the largest peer-to-peer company, it has 75,000+ active investors who have lent over £3 billion to borrowers. It provides incredibly easy to use standard and ISA investment choices appealing to a wide range of peer-to-peer lenders, offers investment products where money is lent to UK consumers looking for personal loans. We are firmly committed to Python is used as a key language in their technology stack and have big plans for the future. Also, they use Flask, Django, RabbitMQ, Pandas, Celery, Postgres, etc.
- Affirm. Its mission is delivering more accountable and accessible services to consumers. Shoppers have the ability to pay for purchases across multiple months with transparent, fairly-priced fees built into every payment, and increases conversion and basket size for e-tailers at less than the cost of credit cards. It is a highly secure solution with flexible payback terms. You can save money and improve your credit score by paying back your loan early with no prepayment penalties. Software engineers use primarily Python/Flask when developing.
- Thought Machine. It aims to cure one of the banking industry's primary problems: its reliance on outdated IT infrastructure and give their customers/users the service they deserve and look for. That’s why they have developed Vault OS - a microservice API architecture platform that makes use of the most recent technology, such as cloud infrastructure and blockchain technology, to create a bank operating system that allows banks to maintain a ledger. This platform delivers a new and unique experience for banking organizations.
- Robinhood. A stock trading app works great for stocks and ETFs and recently added support for Bitcoin virtual currency. Ith key features are focused on tracking stocks you own and on your watchlist. To trade, tap on (or search for) any stock. Enter your trade into the app and own the stock without any commissions or trade fees. It has a premium account that provides access to margin trading and extended hours trading in addition to all other features.
Bottom line: Are you ready to use Python for Fintech?
With a vast number of tools, Python is here to stay in finance. It provides excellent opportunities to solve tricky and challenging issues in financial organizations and automates tedious paper-based processes improving productivity and increasing efficiency. Having a great background in building software solutions that are efficient and successful in the financial sector, we, at DDI Development, apply Python technology due to its numbers of benefits easy-to-use and read syntax, holistic development approach, and usability. Do not hesitate to contact us if you have any idea and need a consultation.