Entercard needed advanced machine learning for credit risk modeling but faced security restrictions on open source packages from public repositories.
Anaconda’s curated, secure package repository satisfied IT requirements while seamless Snowflake integration eliminated development-to-production bottlenecks.
Why Anaconda
Entercard stands as one of the Nordic region’s leading credit market companies, serving over 1.7 million customers across Sweden, Norway, Denmark, and Finland. Founded in 2005 as a joint venture between Swedbank and Barclays Principal Investments, this Stockholm-based financial institution employs over 450 professionals representing more than 40 nationalities.
When Nicholas Munford joined Entercard seven years ago, the company was relying on traditional statistical software for their critical credit risk modeling. As the foundation of their lending decisions, these models needed to be both highly accurate and rapidly deployable—but their existing toolchain was becoming a bottleneck.
“We wanted to start using more sophisticated machine learning techniques like gradient boosted decision trees, which are very useful in our industry,” explains Munford, senior decision science analyst who builds the statistical and machine learning models that assess credit worthiness for Entercard’s applicants. “The support for those kinds of techniques in our previous platform was pretty patchy.”
We wanted to start using more sophisticated machine learning techniques like gradient boosted decision trees, which are very useful in our industry. The support for those kinds of techniques in our previous platform was pretty patchy.”
Nicholas Munford
Senior
Decision Science Analyst at Entercard
Finding the Right Foundation
Entercard’s decision science team faced a classic enterprise dilemma. While they needed access to cutting-edge open source packages for advanced analytics, their information security team wasn’t comfortable with unrestricted access to open repositories.
“We encountered some initial hesitation from our information security departments,” Munford recalls. “They were happy to give us a Python installation, but understandably cautious about allowing us to install whatever open source packages we wanted to. There were potential security considerations from just installing anything from an open repository with no controls.”
The alternative—seeking approval for packages individually through a formal process—would have significantly slowed down a team that needed to move quickly in the fast-paced credit market.
After evaluating various options, Entercard chose the Anaconda AI Platform specifically for its trusted repository of curated packages that could satisfy their security requirements while providing the flexibility their analysts needed.
“The main reason we went with Anaconda was that it gave us access to this curated package repository that we could implement through our information security process,” Munford explains. “Then analysts could install whatever they wanted from that repository—we didn’t have to seek approval for packages one by one.”
This approach proved both cost-effective and practical. While other providers offered comprehensive cloud-based analytics environments, they came with significantly higher price tags and more complexity than Entercard needed for their focused use case.
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