April 23rd, 2019
Is Python really the heart of Fintech Startups?
Python is encompassed by an energetic network of enthusiastic developers who add to open-source libraries, assemble functional tools, and hold endless occasions to share learning about the accepted procedures of Python development. With the sporadic dynamics of the financial industry, the need to move from an old coding language, Java, became inevitable. Python, with its dedicated libraries for financial services, contemporary coding facility, and ever upgrading functionalities, joined hands with the technological domain of financial industry, in order to replace the outdated methods of providing financial services.
How dynamic is the Fintech Industry?
The financial industry involves two areas. The one where old and traditional financial services are carried out with the aid of centuries-old methodologies involving cash payments and the other where people actually want to make cashless transactions through online banking and other digital modes of transactions. Fintech is a large pool of innovative modes of managing and transferring cash with the help of technology and the complexity of this industry is evident from state-level regulations, integrations and mutual payment procedures agreements within companies, bank APIs and mobile banks.
Python proving its mark in the Fintech Industry
With the trajectory of innovation, Python promises these transactions to be made highly secure, easy to use and adaptive to changing demands resulting in:
a) Amelioration of financial services
Python is the best-fit language for the financial industry. Ranging from mobile banking to hedge funds companies, banks are utilizing Python to deal with quantitative issues for trade and risk management and pricing. Python likewise appears to have answers to most challenges raised by the e-Financial Services industry when taking a gander at analytics, compliance, state regulations, and big data, which are made simple by the access to a bundle of supporting libraries.
b) Fintech Startups marking their success with Python
According to a report by KPMG, the global FinTech sector raised $111 bn in the 2018, making it 10% of the total global IT industry. The fintech startups, that have witnessed this huge upside potential with very little downside risk of technology incorporated into financial services using Python are as follows:
1. Thought Machine
Thought Machine was founded by an ex-Google Software Engineer, using Python. Vault-OS, a pioneer product of Thought Machine uses cloud infrastructure and blockchain to maintain ledgers online. Now, this bank can open thousands of bank accounts without having to worry about data management.
A direct competitor to PayPal, Stripe was founded by two Irish young guys Patrick and John. Seeing the gap in the online payment industry, they developed software using Python and Scala, which allows businesses to integrate with websites and apps in order to process real-time credit card payments online.
Founded in 2013, Kensho is valued at $500 Million. It uses Natural Language Processing through Python creating analytical tools for financial and investment professionals.
“The backbone of next-generation financial services is constantly on the lookout for Python/Django devs to join the internal team.” (CEO Figo)
Figo is Europe’s first “Banking as a Service” platform that allows its partners to connect with more than 3000 banks and financial institutions through APIs developed in Python and Django.
“We are strongly committed to Python as a key language in our technology stack and have big plans for the future.” (CEO Zopa)
Zopa provides P2P lending services by eliminating the banks and middlemen and directly connecting lenders and borrowers. This startup uses Python’s library scikit-learn in its programming.
Python takes over Wall Street’s Technology hub
After the Bank of America, Citi Group, and J.P. Morgan decided to shift their trading systems on software programmed in Python’s libraries, all the Wall-Street
“Bank of America and J.P. Morgan, who largely build most of their trading systems in Python and are actively hiring on the street, are always on the lookout for Python developers, but other banks and financial firms, in particular, Fintechs, are starting to look for more and more programmers who can code in Python,”
said Nick Vermeire, a senior technical recruiter at Pencom Systems.
How is Python becoming the heart of Fintech?
a) Optimization of financial services
In the era of digital disruption, fintech startups have to cater the ever increasing customer demand. Also, most of the Fintech Startups in initial phases of funding do neither have much time to market their product nor have enough budgets to pay for overheads. Python makes the job easier by alleviating the time taken to code and deploy the software. Python is known to take half the coding space as that of Java or C++ which also makes the software, more readily available to the market. It not only cuts time but also the cost of hours, a developer will charge for extra coding hours while programming in any language other than Python, making Python a match made in heaven for Fintech Startups.
b) Compatible building of marketable MVPs
MVP is a baby for any startup, the first born child ready to face the world. Building an MVP is highly crucial for Fintech Startups and with Python, solving problems that are in the path of creating compatible MVPs has become much more efficient.
c) Multi-faceted utilization in the financial industry
As Artificial Intelligence sets its feet in the global software industry, Python has taken a pivotal role in its applications in the Fintech Industry. From risk analysis to online payments, from contactless banking to blockchain, Python is set to serve the industry in all dynamics through quick compilation. Python provides the following libraries, specifically for Fintech services, unlike any other programming language and these are:
- zipline (a Python algorithmic trading library),
- quantecon.py (library for quantitative economics),
- pyfolio (risk and portfolio analytics),
- pyalgotrade (algorithmic trading library),
- pyrisk (common financial risk and performance),
- pybitcointools (Bitcoin-based Python ECC library),
- ffn (a financial function library for Python),
- pynance (open-source software for retrieving, analyzing, and visualizing data from stock and derivatives markets).
- finmarketpy (library for backtesting trading strategies and analyzing financial markets),
- SciPy (library for scientific and technical computing),
- NumPy (fundamental package for scientific computing),
- pandas (flexible and powerful data analysis/manipulation library),
- scikit-learn (ML-based algorithms)
d) Bridging barriers between economy and data science
Economics heavily involve statistical data analytics and predictive data modeling which is coded in Python. Clustering analysis for supervised and unsupervised learning in financial modeling involves python libraries for running and representing results. Linear and logistical regression through python helps Fintech Startups map their data in the right manner and fetch plausible interpretations of statistical algorithms.
Future Outlook of Python’s relationship with Fintech industry
With the outburst of disruptive innovation, Fintech industry is expanding in countless new streams such as cryptocurrency, bitcoin, blockchain, ethereum, regtech, insurtech, open banking and financial inclusions, Python has promised to be ready to complement and improve its libraries with all these new emerging technologies.
We now have a clear idea of how adopting Python is going to result in the sustenance, innovation, and growth of Fintech Startups. We would like to quote HackerRank here:
“Python wins the heart of developers across all ages, according to our Love-Hate index. Python is also the most popular language that developers want to learn overall, and a significant share already knows it.”
Based on this quotation, it is safe to assume that Python is really the heart of Fintech industry with all that has to offer this huge industry through its dedicated libraries and coding characteristics.