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.

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 good old traditional finance is still maintaining the stern financial system. 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.

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.

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.


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.

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.

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.

Python is embedded in CaseWare IDEA, so by using IDEA you will come to reduce risks in the company, as well as reduce errors. Python is the best language for optimal time market and has the best open source libraries. Remember, companies that want to compete need high-quality products.

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