Capital markets team spent hours daily on manual Excel workflows, faced $2–3M third-party software costs, and needed scalable, audit-ready compliance automation.
Built an Excel-Python-Excel automation framework on Anaconda Platform, eliminating manual workflows, avoiding $2–3M in costs, and delivering audit-ready compliance outputs.
A Fortune Global 500 financial institution and one of North America’s largest banks, serving millions of clients with over $700 billion USD in assets, had an Executive Director overseeing capital markets operations who faced a familiar but costly pattern: manual processes consuming analyst hours daily, escalating compliance complexity, and third-party software quotes in the millions. The bank’s capital markets team needed a faster path from data to decision, without the price tag or the risk.
The Executive Director, overseeing trade floor supervision for the bank’s commodities desk, recognized that manual Excel processes were not only consuming hours of his productive time each day, but they were also creating scalability limits that would only worsen as data volumes grew and regulatory requirements increased.
“It was like the Sorcerer’s Apprentice,” he explains. “There were just so many Excel files, both coming in and coming out, that I simply needed something to help me automate what I was doing.”
The Challenge: The Cost of Manual Processes at Scale
The capital markets environment generates massive volumes of data that require daily analysis, reporting, and compliance monitoring. For this Executive Director, the challenge manifested in several ways:
Time-Intensive Manual Workflows: Manual workflows consumed hours daily. Each task required opening multiple Excel files, copying data between spreadsheets, running formulas, filtering columns, hiding information.
Operational Risk from Manual Workflows: Each daily task required opening multiple Excel files, copying data between spreadsheets, and running formulas by hand. In a regulated environment, every manual step is a potential compliance failure.
Unsustainable Scale: As trading activities expanded and regulatory requirements increased, the volume of manual workflows grew with them. The process did not scale.
Multi-Million Dollar Third-Party Alternatives: Commercial software to solve these challenges carried price tags of $2-3 million, with no guarantee of fit.
The Executive Director needed a better approach, one that could automate repetitive workflows, reduce errors, and scale with the business without massive technology investments.
Finding the Right Foundation
When the executive proposed building an alternative to expensive third-party software, a colleague offered simple advice: “You’ve got to learn Python. It was written for people like you. It was written for non-coders. And the best way to get started is to download Anaconda.”
The recommendation proved transformative. The Anaconda Platform gave the team a standardized, enterprise-ready foundation. Pre-configured environments, a comprehensive package ecosystem, and consistent tooling meant the team spent time solving business problems, not setting up infrastructure. The number of implementation barriers was minimal.
“Anaconda gives you all these additional things that you as a beginner don’t even realize that you’ve downloaded, but it’s helping you out,” he explains. “The number of speed bumps was minimized.”
Anaconda’s integrated development environment, Spyder, proved particularly valuable for learning. It allowed him to see code on one side and results on the other, enabling rapid iteration and immediate feedback on what worked and what didn’t.
Anaconda’s comprehensive package ecosystem included pandas, a Python library specifically designed to replicate Excel functionality in a programmatic environment. This meant the executive didn’t have to abandon Excel entirely; instead, he could enhance it.
From Manual Workflows to Automated Intelligence
The breakthrough came in developing what the executive calls an “Excel-Python-Excel sandwich.” Rather than replacing the familiar Excel interface, Python automation enhanced it:
- Excel files remain the inputs, arriving via email or network directories exactly as before
- Python reads the data, performs all the transformations, calculations, and analysis that previously required manual mouse-and-keyboard work
- Results export back to Excel format, ready for distribution to colleagues who continue using their familiar tools
This approach proved particularly powerful for position limit review, which is a critical Exchange Traded compliance requirement. The team receives reports with embedded alert schemes that weren’t fit for purpose. Using Python, they now automatically process these files and generate customized heat maps based on their specific risk parameters.
The result was a workflow model that required no disruption to existing processes. Teams continued working in familiar Excel interfaces. Anaconda handled the transformation, analysis, and compliance reporting automatically in the background.
“Every daily routine thing I do is now performed in an Excel to Python to Excel workflow,” the executive reports. “I can take this file automatically, take the data and come up with our own green, yellow, red heat map based on our own specifications.” This gave the team audit-ready outputs based on their own risk specifications, produced automatically.
Measurable Outcomes
The results exceeded all expectations. Within three months of starting to learn Python with Anaconda Platform, the executive delivered a solution that eliminated the need for $2-3 million third-party software purchases.
The measurable benefits include:
Millions in Cost Avoidance: The team built internal automation that replaced the need for $2-3 million in third-party software. The solution was operational within three months.
20% Year-Over-Year Efficiency Gains: Consistent productivity improvements through automation have compounded over time, with standardized workflows continuing to deliver value.
Reduced Error Rates: Standardization through automation means fewer mistakes. “My Excel, I can’t remember the last time my Excel file kind of got stalled because they do all the work outside of the interface.”
Scalability Without Headcount: The automated workflows handle growing data volumes and complexity without requiring additional staff.
Hours Saved Daily: Tasks that previously consumed entire days now complete automatically. “I saved myself hours a day of work that I would have had to have done otherwise,” he notes.
Perhaps most importantly, the solution included built-in governance. Python scripts could be emailed directly from automation, creating audit trails and documentation that satisfied regulatory requirements.
Addressing Governance and Compliance
For regulated industries, governance is not optional. The team built a tiered approach that allowed them to move fast in development while meeting enterprise compliance standards at scale. When developing a new workflow or work stream, a developer starts at the bronze level to quickly respond to immediate business needs. As the solution proves valuable and usage expands, it naturally progresses through silver to gold as governance requirements increase. This evolutionary approach allows teams to move fast while building toward enterprise-grade compliance:
Bronze Level: Individual creates Python automation to address immediate needs. Equivalent to an Excel file with macros, owned and operated by a single subject matter expert.
Silver Level: Multiple team members use and maintain the solution. Introduces segregation of duties and broader responsibility, with documented processes and version control.
Gold Level: Central technology team takes ownership. Full enterprise governance, professional development standards, and integration with corporate systems.
“This framework helps communicate our plan,” he explains. “We’re doing the bronze level because we’re responding to a new regulation, or the desk is trading a new product. Once we get to the silver status, this is actually the equivalent of a business requirements document. Central Tech can read my code better than they can read my writing.”
Looking Forward: Scaling Across the Organization
The success of this project created a replicable model for automation across the organization.. Other teams are now exploring similar approaches to streamline their workflows, with the executive serving as an internal advocate and advisor.
“The entire industry is looking at Python more and more, particularly for people like non-coders like myself,” he observes. “We’re asking Excel to do a lot right now.”
For organizations considering similar initiatives, his advice is straightforward: start with Anaconda. “For people like me, it’s a distribution platform. That means it’s a platform. It’s steady, it’s rock solid. People know how to get to it. Everyone gets onto the same version of the software.”
The executive also emphasizes the importance of thinking evolutionarily about code. “Once a coder writes something good, the first draft is only the first draft. You have to refactor your code, document it, and make it modular so that when a new product or problem appears, you can easily scale.”
Most importantly, he notes that automation isn’t about replacing human expertise but rather about amplifying it. “The Gold Standard ought to be that I get an alert and use my industry experience to determine whether or not it’s a problem, not be the coder. The reason I coded was because I needed it.”
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