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How Open-Source Software Such as R, Python, and Vim Have Helped Fintech Companies Predict the Future

R Python And Vim Can Help Fintech Companies Predict The Future

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Ray FitzGerald
By: Ray FitzGerald
Posted: May 7, 2018

Our experts and industry insiders blog the latest news, studies and current events from inside the credit card industry. Our articles follow strict editorial guidelines.

In a Nutshell: Two decades ago, data analyst jobs required proficiency in Statistical Analysis System (SAS) programming. But as the cost of SAS desktop boxes became prohibitive, many institutions began using free open-source program languages that offer greater flexibility and compatibility. Large corporations, including American Express and Bank of America, now look for data science applicants with skills in R, Python, Postgres, and Linux. Many of these organizations use the open-source Vim text editor — one of the most popular text editors among coders in the financial industry — as the vehicle to drive innovation. And the combination of Vim and R helps create risk assessment models for credit card lending, predict future returns on investment portfolios and pension plans, and build alternative credit scoring models.

Twenty years ago, financial giants such as American Express sourced their data scientists from unexpected places. With stiff competition for graduates of the typical college programs, AmEx often turned to agricultural economists.

While these students trained in economic theory to optimize the production and distribution of food and fiber, they also had another in-demand skill that set them apart: a proficiency in programming SAS.

SAS is still the gold standard among many large firms, but the exorbitant costs of the monolithic, closed-source program prices many small- and medium-sized businesses out of the game. Those financial firms still need skilled data scientists to run their quantitative risk analysis models — but they need it done affordably.

The rise in free, open-source languages such as R, Python, Postgres, and Linux created a seismic shift in the way companies process big data and changed the requirements they have for new hires.

Mastering these languages means data scientists can take their skills anywhere and perform analysis without having to ask permission to purchase a pricey SAS desktop box.

R and Python boast complete transparency of the types of functionalities and algorithms a user can leverage. The Comprehensive R Archive Network provides extensive packages as free downloads to meet the needs of almost any job.

Vim Logo

Many businesses of all sizes opt to write their coding on a similar open-source platform by using the Vim text editor.

Vim made its public debut in 1991 and has since grown to become one of the go-to choices for coders. Our development team at uses Vim to write the code that delivers financial news to your devices.

Extensive customization options make Vim attractive to data analysts of all experience levels. Users have several options to control the basic interface and can define personalized key mappings — also known as macros or abbreviations — to automate sequences of keystrokes, or even call internal or user-defined functions.

Vim’s flexibility makes it a popular choice for financial services firms because it supports scripting using Lua, Perl, Python, Racket (formerly PLT Scheme), Ruby, and Tcl.

Open-Source Technology Can Save Time and Money

For all of its benefits to data analysis, the SAS price tag makes it unattainable to most companies. The closed-source nature of the program also limits its capabilities.

R and Python are free programming languages for data analysis, statistical modeling, and visualization. The financial world leans on the languages as the two most popular tools for predictive modeling and risk analysis.

A 2016 data science salary survey conducted by O’Reilly Media ranked R second, just behind SQL, in the category of best programming languages for data science. A KDnuggets Analytics software survey poll listed R as the top-ranked language.

R can perform the same data analysis and data science tasks SAS does, but without the need to purchase a bundle of SAS software and modules. Users leverage R to create interactive visualization models, ensemble and machine learning, text and social media mining, and, most importantly in the financial world, optimization and forecasting.

A List of Companies that Use R

The list of financial institutions using R is as impressive as it is long — and includes heavy hitters such as Bank of America, Barclays Bank, Citibank, and JP Morgan.

In the healthcare industry, advanced analytics help experts understand performance and predict future outcomes in rapidly changing care and payment models. Microsoft expressed the importance of R in those outcomes in 2016 when the company acquired Revolution Analytics, the leading commercial provider of software and services for R.

“At Microsoft, we are embracing and extending the use of R in healthcare worldwide,” said Tom Lawry, Director of Worldwide Health and Global Product Strategy at Microsoft in a press release announcing the acquisition. “In the past year, we did this by delivering a steady stream of innovations and updates to help our customers and partners leverage the power of R.”

Vim: An Open-Source Universal Text Editor

For scripters, working without a proper text editor is like trying to write a book using a pen with no ink. Flexibility is important in any text program, as is the ability to customize settings to match the needs of the user.

Vim’s founder, Bram Moolenaar, focused on both flexibility and ease of use when he created a variation of Bill Joy’s vi text editor program for Unix in 1991.

The editor quickly branched out to support multiple platforms after its original release on the Amiga computer. The name Vim derives from the editor’s improvement on the vi editor — hence Vi IMproved — with many additional features designed to be helpful in editing program source code.

A Vim Screenshot

Vim is known for its flexibility, innovation, and ease of use.

Like vi, Vim’s interface does not use menus or icons. Instead, users type commands in the editor’s text box to call up features. The program’s GUI mode, gVim, adds menus and toolbars for commonly used commands, but the command line mode still expresses the editor’s full functionality.

The :vimtutor command opens a built-in tutorial that walks new users through important program features. The Vim Users’ Manual also highlights aspects of the program and can be read from within Vim or found online.

An extensive list of plugins extend or add to Vim’s functionality. The open-source nature of the program allows users to write these complex scripts in Vim’s internal scripting language, vimscript.

Enabling Finserv Institutions to Make Smarter Decisions

Programmers who build data models have been at odds for years when debating between R and Python as the most effective language. Both certainly have higher adoption rates thanks to their open-source coding and low price point (you can’t get much lower than free).

Financial service providers use both languages because they allow data models to have very brief time to market. Each language integrates easily and adds value to existing systems thanks to thriving communities of programmers who push the envelope of innovation.

Several worldwide pension plans use the R programming language to forecast interest rates, investment returns, and estimate life expectancy and retirement age using probability models, data analysis, quantitative, and statistical methods.

Credit card issuers and other lenders use R programming to manage risk and create unique credit scoring models through the use of recursive partitioning trees.

In many cases, Vim is the text editor of choice for programmers at finserv companies. As with the languages used to analyze data, the Vim editor boasts the same open-source flexibility and innovative interface users have come to expect from innovative scripting solutions.

Vim helps enable the development team here at as well as those of some of the largest financial and investment firms in the world create models that predict the future and mitigate risk.