What is Fintech?

Financial technologies and companies, commonly known as ‘Fintech,’ have been defined as “businesses that leverage new technology to create new and better financial services for both consumers and businesses.” The Fintech industry continues to grow, with investments continuously growing since 2010. From 2017 to 2019, the value of investment into financial technologies doubled.

What is Python?

Python is a high-level programming language with a wide range of usability. It is the most used programming language, used as the primary coding language by approximately 80% of developers. Companies running on Python include Facebook, Netflix, and Intel.

Python is preferred over other methods due to its ease of use combined with its multi-purpose solutions.

Technological Challenges in the Fintech Industry: How Python can Help

There are several challenges that the Fintech industry faces every day. Below are explanations of these issues and how Python can be used to counteract these problems.

Data Security

Concerns with virtual security are an ever-growing issue. Fintech is unable to be guarded as traditional banking systems, and hackers can immediately drain accounts and receive rewards. Python can easily implement data encryption and utilize security features such as two-factor authorization and biometric authentication. Python also allows for banking software to be created more efficiently and cheaper than other options.

            Ease of Use & Learning and Integration

An issue in financial technology is the lack of expertise in creating user-friendly app development services. Python allows fintech developers to focus on the structure of the software and its complexities rather than maneuvering the coding language itself. This also allows for collaborations and integrations with other services, such as budgeting and banking apps. The language and libraries also have analytics solutions for the layperson, the primary one being the digital visualization tools. The ease with which Python is used is the primary reason for its successful utilization in the Fintech industry.

AI Integration and Machine Learning

Machine learning in the financial technology industry is beneficial for automated trading, fraud detection, regulatory compliance, customer service, and more. Using AI technology also allows organizations to collect personal information and the financial behavior and habits of users- this allows for automated management of transactions and risks analysis. Implementing AI technology for Fintech is an issue due to the fact that it requires constant maintenance and exceptional knowledge and skill. The Python language allows for large amounts of data to be processed, making machine learning simple. Python also has an extensive pre-built library selection, helping to implement more functionality with less code. Listed below are some of the libraries that Python utilizes for AI and machine learning:

  • NumPy
  • Pandas
  • FinmarketPy
  • PyFolio
  • SciPy
  • Pyalgotrade

We face many challenges in the FinTech industry, but utilizing Python will counteract these issues as it has proven to be useful in almost every avenue for financial technology.