Python & Machine Learning for the Financial Industry

Python has recently overtaken R as the most commonly used solution for machine learning, with its versatility, cross-platform support, and end-to-end use. As recent studies have shown, the financial industry is increasingly investing in ML to solve key issues; check out our datasheet for more info.

Python-Machine-Learning-for-the-Financial-Industry

Actionable Insights That Fuel Business Growth And Profitability

Growing regulatory requirements, pressure to cut costs, and decreasing margins continue to be key market drivers for banks and other financial institutions. Digital transformation has helped address some of these issues in the past, but traditional solutions are faltering in the face of an ever-growing mountain of customer, market and industry data.

Static, manually managed, and quickly out-of-date Excel spreadsheets can no longer keep up. What’s required is a new solution that can reflect emerging trends with interactive, up-to-date solutions that can work with big data.

As a result, banks and other financial institutions are increasingly investing in Machine Learning (ML) in order to deal with ever expanding volumes of data that traditional analytical methods can’t deal with effectively. And ML is more and more viewed as the domain of the Python programming language.

Why Python?

Python has recently overtaken R as the most commonly used solution for ML. Whereas R is still popular among statisticians and general data science applications, Python now incorporates the bulk of all ML libraries, including Google’s TensorFlow, Facebook’s PyTorch and Microsoft’s Cognitive Toolkit.

In fact, Python is where the majority of the free/libre and open-source software (FLOSS) community is focusing its efforts around advancing ML.

As a general rule of thumb, open source solutions provide organizations with the greatest agility and control over their ML initiatives, but require strong in-house skills. By comparison, commercial solutions allow less skilled organizations to get started right away, but may prove limiting if you’re attempting to create white space from your competitors.

For financial institutions who may be focused on more traditional Java technology stack, Python provides a number of additional advantages, including:

  • Versatility & Speed: Python is much quicker for building everything from simple scripts to large applications; from low-level systems operations to high-level analytics tasks.
  • Cross-Platform Support: Python is available for all important operating systems, including the Windows, Linux, and macOS systems your teams prefer.
  • End-to-End Use: For ML projects, Python is commonly used from prototyping to production, avoiding the traditional handoff between data scientists (using R) and programmers (using Java) that can delay time to market.

Machine Learning In The Finance Industry

As recent studies show, the financial industry is increasingly investing in ML to solve key issues, including:

  • Profitability: ML can help optimize the execution of trades via trade simulations and automation of transactions.
    • ML in insurance markets can better analyse the complex data that determines pricing and market insurance contracts in order to lower costs and improve profitability.
  • Risk: ML can reduce the number of false positives associated with detecting instances of money laundering, financing of terrorism and fraud by replacing simple, rules-based pattern-matching with more sophisticated algorithmic approaches.
    • ML-based cybersecurity systems can analyze patterns and learn from them to help prevent similar attacks and respond to changing behavior.
  • Revenue: Banks often have numerous clients with diverse needs, but fewer advisers to service them, resulting in reduced client coverage. ML-driven “recommendation engines” can provide clients with better, more personalized options faster than traditional methods.
    • ML-based sentiment analysis can determine consumer preference for specific companies and stocks in order to make better recommendations to clients.
  • Customer Support: ML can help automate client interactions and customer support with chatbots, which lower costs while helping customers solve problems.
    • ML-based predictive banking provides customers with reminders to transfer money, automate recurring payments, or set up a travel plan for their account after they’ve purchased a plane ticket, etc
  • Compliance: In the wake of the 2008 financial crisis, ML can help address the need for regulatory stress testing by calculating potential losses for a given default, as well as the probability of default models.
    • ML can interpret financial and legal documents, such as bank statements, tax statements, contracts, etc to help gain insights into a customer’s financial health.

Datasheet Python and machine learning for finance graphic

Python Use Case in Fintech

An American multinational financial services corporation headquartered in New York City wanted to accelerate their digital transformation in order to put themselves at the forefront of the digital revolution. By mining complex digital customer and prospect behavioral data, the customer hoped to transform it into actionable information. But such a major business transformation would require a corresponding technology transformation. To that end, the customer initiated a number of data science and machine learning projects to examine the structured data they’ve been collecting for years. The customer then correlated the structured data with unstructured data from web and social media.

A single, standard, data science-focused build of ActiveState’s Python distribution, ActivePython, for AIX, provided all of the data engineering and data modeling capabilities required. Using ActiveState’s Python, ActivePython, ActivePython, the customer was able to combine their transactional data with social media (such as Facebook and Foursquare) data in order to identify when a customer was preparing for a vacation. Those customers were then offered cross-sell services such as travel insurance, foreign exchange, etc.

As a result the corporation was able to significantly increase cross-selling & reclaim resources.


Looking for commercial support, older versions of Python, or redistributing Python in your software? We’ve got you covered on the ActiveState Platform. Compare pricing options in detail or contact us for a custom quote

An enterprise can accelerate data science and software development with secure, supported Python and the robust support of an open-source company like ActiveState.

Related Resources:

ActiveState Platform: Get Python Applications to Market Faster

Top 10 Python Use Cases

Katie Chang