use of python in finance

Python is a general-purpose language, and it can be used to build almost anything. If that the case, what are some examples of the use of Python in finance services. Financial industry is considered  one of the most intricate industries where the security is uncompromising. Java has been dominating this industry for this reason. However, Python has become the most popular language for fintech industry.

Python has simple syntax which allows rapid deployment of applications. Moreover, Python has abundance of libraries and tools which can be easily integrated into the system. The popularity of Python also means that Python has a very large community and it makes recruiting processes easier. Python flexibility makes the use of this language in finance services so extensive. It can be adapted to build various banking applications, customers virtual assistances, risk managements, data analyses, stock market analyses, predictions, cryptocurrency services, pricing and trade management.

Tech Challenges in Finance

In today's business, financial institutions cannot evade technology anymore. In fact, they are more and more look like tech companies than merely a finance firm. In order to thrive in this industry, a finance company must adapt a technology that is exceptionally flexible.

The technology must comply to tight regulations, yet provides flawless integration with other services and business partners. On the other hand, it needs to attract and maintain loyal customers. A financial institution will always be competing with their rivals to satisfy customer demands, deliver personalized experiences and added-value services.

Finance institutions also recognize two opposite societies. The good old traditional finance is still maintaining the stern financial system. Even though it embraces new technologies to a certain degree, but it anticipates it little to no impact to the industry. It is proved to be strenuous to challenge their old and rusty practices.

In contrast, millennials accustom using cards, online banking, mobile wallet and various digital financing services. They are well prepared for any new technology and its consequences on finance. This generation is ready to operate highly technological fintech but the old-school bureaucracy is in their way.

Fortunately, they perceive a wider space in developing countries to implement technological solutions to financial sector. This reality arises because most developing countries have no imperturbable financial policies. Fintech is slowly but surely grow and develop in emerging nations. They eventually enable people in developing countries building business with the developed world by using secure online transactions.

Another challenge comes from data, not just common data but BIG DATA. Data come from their end users, competitors, markets and much more. Millions of transactions conducted by customers generate very large data. These myriad data would mean nothing if we could not summarize it into meaningful information.

The financial services sector must always be agile and responsive to whatever challenges they encounter. Choosing the right tech stack is undoubtedly one of the most substantial phases before they operate a finance technology. Among many options, Python has shown encouraging advancement in this industry.

Python is an interpreted language, hence it is considered slower than compiled programming languages like C, C++ or Java. However, Python showed substantial growth in term of popularity and usage especially in finance industry. According to eFinancialCareers, the demand for professionals with Python skill has jumped almost three times over a year period. HackerRank reported that in 2018 Python was among the top three most in-demand skill in financial services.

Pyton in Financial industries
Source: HackerRank

You can start learning Python by studying the core elements of this Language. Lean Python book equips you with most-used functions in Python, which are all you need to know as a beginner.

Giants in the financial world who use Python

While Python has been around since 1990, but its prevalence in finance industry is a relatively new development. Multinational investment bank and financial services companies like JPMorgan, Bank of America Merrill Lynch and Citigroup Inc are started embracing Python.

JPMorgan claimed that they are a technology group, not a bank. They allocate $10.8 billion annually on tech, the highest among other Wall Street groups.

JPMorgan has their own cross-market risk management and trading system called Athena, which was built with Python and C++. Athena is now accessible by JPMorgan's clients. Interestingly, they are not only built software, but also dispatch hundreds of their investment bankers and asset managers to a mandatory coding lesson. They want their junior managers are able to analyze extremely huge data sets and interpret unstructured data.

Bank of America Merrill Lynch (BAML) doesn't want to be left far behind by JPMorgn and Goldman Sachs who already had SecDB (it uses Slang programming language). BAML is developing their own platform named Quartz which uses Python language. This integrated trading and risk management platform is proved to be far more efficient than having multiple risk system.

One of their developer unveiled that Quartz uses Python because this language has many aspects of other languages, but few languages which has so many features like Python has. One component that was developed extensively is a foreign exchange (FX) product pricing tools. It will help their traders  come up with better decisions.

BAML doesn't have a mandatory coding lesson, but they support their tech managers with astounding learning resources. They have online tutorials, coding challenges and live lectures.

On the other hand, Citigroup is more interesting to support a scientific computing software Anaconda. Anaconda is an open source and cross-platform distribution of Python and R languages for data science, artificial intelligence, machine learning, etc.

Citigroup also has Python training programs for their investment bank analysts.

Moreover, eFinancialCareers also revealed that Python was the second most wanted programming expertise by five big U.S. investment banks on December 2018. Python (350 vacancies) was behind Java (529 job openings) for total available positions across Wall Street.

Goldman Sachs also needed more Java developers (196 positions) than Python (109 jobs). Similarly, JPMorgan opened 208 openings for Java and only 158 vacancies for Python. Moreover, Morgan Stanley needed 73 Java programmers compared to 48 Python experts. In contrast, Python beat Java as the most in-demand language in Citigroup (33 vs 28 positions) and Bank of America (30 vs 24 jobs).

Why so many companies use Python in finance services

Python is as simple as plain English 

Python has a very simple structure and easily defined syntax. Python removes superfluous symbols like semicolon and curly brackets, and uses indentation to delimit code. This makes Python absolutely easy to read because it so similar to plain English.

Python is also a dynamically typed language. This means that you don't have to declare anything. A statement of assignment binds a name to an object, and it can be of any type. Correspondingly, Python code becomes so concise and compact, expressing much in a few words.

The simplicity of Python decreases the potential error created by developers. As a result, programmers can avoid wasting countless hours fixing bugs caused by missing or misplace a semicolon or a curly bracket.

People who start learning Python also find that this language is easy to learn. It is not only for students and programmers with non-Python background but also professionals who have non-IT background. Accordingly, Financial firms save a lot of time and money by providing concise and high valuable training programs.

Moreover, the simplicity and readability of Python enable convenient collaboration between staffs who have different backgrounds. Code review becomes less time consuming and the amelioration of the feedback can be done quickly.

“Now you have people in different areas of the business that can collaborate effectively on the same piece of code, bringing different areas of the business together,” - Gary Collier (Man Group Alpha Technology)

Python boost productivity and speed up time to market

Python is widely regarded as a slow programming language. Programmers consider Python is slow because Python is an interpreted language and Python is dynamically typed language. Python abstracts the details of the computer (pointer, memory management, etc.) from programmers. Thus, execution time of Python codes is 10 times or more slower than compiled languages.

However, Python is considered as one of the fastest languages when it comes to product delivery to market. When you are on a specific budget, you need to quickly validate your product on the market. Python is best suited since it offers quick deployment and comes with less coding required.

Python programming language has other features such as clean object-oriented design, enhanced process control capabilities and its own unit testing framework. Those aspects support developers productivity.

Python has a rich ecosystem of tools and libraries

Python developers don’t need to build all of their product components from scratch. Python communities have already contributed so many common functions stored as Python libraries.

Suppose that you need a program to calculate the due date of a certain payment before a customer receives a penalty. You don't have to write hundreds of lines of code calculating hours, days, months or years if the due date is on January next year. All you have to do is import a library called Datetime and call several readily available functions.

This saves organizations both time and money. Fintech products also require integrations with third parties and python libraries come in handy. It has more than 166,000 third-party libraries (and growing quickly). You can develop a product just like joining building blocks. You can find a full list of Python libraries for finance industry in this website.

Thanks to the development speed of Python combined with the collection of libraries and tools, this builds a competitive advantage for organizations that are keen to address ever-changing consumers necessities.

Python connects Economists and Mathematicians

While there are numerous languages for math experts (Mathlab, R, Python, etc.), most economists only familiar with easy and fast to learn programming language like Python. Python has become the programming lingua franca for these two worlds.

This is very important because all parties involved in a development project must complement their skill set with other persons who have different roles. Python is the right choice for multidisciplinary projects that required reviews in each phase from all team members.

Ready to learn and implement various Quantitative Finance concepts using Python libraries? Python for Finance second edition will help you to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM's market risk, running a Fama-French 3-factor, 5-factor, or others.

How Python is used by financial industry

Banking Software

The banking sector faces a massive challenge in the form of disruption from the world of fintech. Traditional banking must adapt swiftly by embracing technology to deliver applications for their corporate banking, mobile banking, payment gateway, risk management and asset management.

Python syntax works really well with mathematical calculations and algorithm. Banking companies will obtain the same results with fewer programmers if they choose Python. Additionally, they can minimize adapting different programming languages, because Python is able to achieve things not possible with other programming languages.

On the other hand, fintech startups nimbly develop digital banking services. They usually start with one solution like online payment, lending services, international remittance or digital banking services.

One example of an online payment platform that uses python is Venmo. It combines payment service and social media platform. The app allows you to transfer money to another account, make a payment in a shop or chat with your friends.

Stripe is a mobile payment system which was built with Python, Ruby and Scala. Python libraries enable Stripe to have secure authentication processes and auto-pagination. Stripe is gaining a great success and handling payment for Amazon,, Shopify, Salesforce and Facebook.

A startup company which use Python to build a peer-to-peer (P2P) lending platform is Zopa. This platform allows borrowers and lenders make a deal without intermediaries. Zopa has exceeded £3 billion in lending in early 2018, and allows a borrower obtaining up to £25,000.

This programming language also comes in handy for developing ATM software.

Customer Service

In this disruption era everything is changing, and it's changing so rapidly. Customers do not want to waste time coming and queuing to banking or finance office anymore. Many consumers discover calling a call center is a tedious and slow experience.

Python Chatbot
Source: BoA
Chatbot has become a new solution to deliver immediate and accurate information to customers. Once built correctly, chatbot can provide digital support 24/7. This is not only a cost saving for finance companies, it is also an engaging experience for clients.

A well designed chatbot may lead visitors of a website to a financial solution that they really need. Thus, opening of new accounts can be increased. Chatbots also store all of customer's data. When it is integrated with artificial intelligence (AI) on the system, chatbot can provide recommendation for better money management.

Bank of America has a virtual assistance called Erica. Erica delivers notifications to customers, suggestions how to save money, balance information, credit reports and assistance of simple transactions.

Other banks which have similar chatbots include Well Fargo, Capital One, Ally Bank, USAA, HSBC (Hong Kong), Hang Seng Bank (Hong kong), SEB (Sweden) and Commonwealth Bank (Australia).

Well, not all of that banks use Python to build their chatbots. But they must bring the natural language processing into play. Python has this advance AI library called NLTK that widely used to build chatbots.

Do you want to start learning how to build a chatbot? Building Chatbot with Python is a great book to start with.

Risk Management

There are always risks in financial industry. Finance companies invest significant amounts of money and time to mitigate risk. As a result, there are numerous risk models to identify which customers are qualified receiving loans and credit.

Nevertheless, translating a risk model into risk decisioning processes proves to be hard. The ability to constantly combine, deploy and maintain existing models would be the key factor for the successful implementation of risk management.

Python is one of the most suited language for this application. Python opens the door to implement machine learning and deep learning for credit risk challenges. Python supports simple algorithms such as logistic regression, decision trees, random forest, support vector machines, and more advanced algorithms such as clustering and neural networks.

Automated Trading

As the name implies, automated trading uses computer program to automate the trading process. It is also better known as algorithmic trading or algo-trading because it follows an algorithm (a set of instructions) for placing a trade.

The ultimate key of automated trading is finding a perfect algorithm which systematically identifies the best trading strategies and thus offer predictive and actionable analytical insights into conditions of specific markets. Furthermore, algo-trading eliminates any influence of human emotions on the trading processes and generates profits which cannot be achieved manually.

Python has come out as the most desired language for automated trading. The powerful and wide variety of Python libraries enable you to perform data analysis (pandas), predict stock market by using machine learning technique (sci-kit-learn) and integrate Google's deep learning technology (tensorflow). Moreover, your analysis can be visualized into appealing charts by using matplotlib and seaborn.

Do you want to explore effective trading strategies in real-world markets using NumPy, spaCy, and pandas? Hands-On Machine Learning for Algorithmic Trading is the right book for you.


If you want to build a cryptocurrency business, you must be equipped with rigorous tools to conduct the analysis of cryptocurrency markets. You must be able to get insights and make an accurate prediction for the future.

Python is a perfect language for cryptocurrency markets analysis because it has data science ecosystem called Anaconda. With the help of this tool, you can obtain  cryptocurrency pricing, analyze it and visualize it into charts.

Consequently, many web applications created for analyzing cryptocurrency were developed on top of Python language. some examples include crypto-signal, Koinim, Dash,  ZeroNet. 

Fraud Detection

Despite the implementation of chip card technology, fraudulent transactions keep haunting banks and financial institutions. Experts forecast that this economic crime will soar to staggering $31.67 billion in 2020.

Fraud detection is always on the top priority for finance industry. In the past, this was a strenuous activity because all transactions must be manually checked by employees. The automation of this process has become attainable thanks to the rise of data science, artificial intelligence, machines learning, and deep learning.

The essence of automated fraud detection is the detection of outliers. Pandas, numpy and scikit-learn are three Python libraries that is commonly used side by side for big data analysis. Accordingly, anomaly detection of cards transaction data can be done by using those libraries. An example of fraud detection can be examined from this website.

Python is a must have language for finance

Financial world is a challenging industry. Companies that want to compete strongly need to develop high end products. These products must be fully compliant with local and international regulations, functioning and secure. Attention to detail must be followed since these solutions are integrated with services, other institutions and API connections that must run smoothly.

Python has a clear programming syntax that matches with an amazing ecosystem of libraries. This makes Python one of the best technologies to handle any development procedures in any type of financial service provider.

Selecting a language that will be core to your finance product will leave a serious implication over the lifespan of products. Remember that python’s simplicity leads to less bug hunting and lower error rates. Python is the best language for optimal time market and has the best open source libraries.