Bank needed to deploy advanced ML for fraud detection but faced tension between open source innovation and strict security/compliance requirements for production systems.
Anaconda Platform provided pre-screened development environment with trusted Python packages, enabling secure ML deployment while eliminating vulnerability retrofitting and maintaining regulatory compliance.
Balancing Innovation and Security: How One Bank Transformed Fraud Detection with Anaconda
Achieving 90% fraud reduction by bridging the gap between open source agility and enterprise governance
Financial institutions today face mounting pressure to detect fraud with precision while maintaining seamless customer experiences. For one bank’s data science team, this challenge intensified as fraudsters became more sophisticated and the volume of transactions requiring real-time analysis continued to grow.
The team needed to leverage cutting-edge machine learning techniques, but their security and compliance requirements demanded strict governance over code dependencies in production systems. This created a fundamental tension: how to move fast with innovation while maintaining the rigorous security standards banking regulators require.
“With AI getting more complex, having loads of disparate data inputs and making sure that the algorithms we deploy are stable and perform as we expect is quite difficult,” explains the Head of Data Science, who oversees a team responsible for developing algorithms across fraud detection, credit risk, and regulatory compliance. “The only way to do that is to use data science. You have to use machine learning to deliver anything that is sufficiently complex.”
Breaking Free from Legacy Statistical Software
Previously, the team was relying on traditional statistical software for their critical modeling work. While their previous platform had served them adequately for basic analytics, it was becoming a significant limitation as they sought to implement more sophisticated machine learning techniques.
“We wanted to start using gradient boosted decision trees, which are very useful in our industry,” the Head of Data Science explains. “The support for those kinds of techniques in our previous platform was pretty patchy.”
The proprietary nature of their existing solution meant the team was entirely dependent on the vendor’s development roadmap. “When you use an enterprise, closed-source language, you’re at the behest of the developers of that language,” they note. “There will be a lot of times when you have to spend a large amount of time doing some bespoke solution, making the code do something that there’s not an inbuilt function for.”
Anaconda’s open source ecosystem offered access to cutting-edge techniques as soon as they became available. “With Python, it’s very unlikely that someone hasn’t solved a problem in a certain way” they explain. “You get all the benefits of open source: newer things are available very quickly, there’s a huge amount of innovation happening.”
The Security Challenge in Financial Services
However, the bank used Python sparingly in their production environment due to security concerns. The team needed access to the latest open source packages for more advanced fraud detection models, but their security requirements demanded strict controls over package vulnerabilities.
“What Anaconda gives us is it allows us to pre-screen our development environment so that we know that when we release into production there are no vulnerabilities,” the Head of Data Science explains. “The problem we had in the past was that we could develop algorithms in our development environment, but we had no idea whether it had any open vulnerabilities.”
The alternative of manual vulnerability scanning after development created significant bottlenecks and potential security gaps. Without proactive screening, developers would often discover vulnerabilities only after committing code, forcing them to stop their current work and spend days or even weeks retrofitting fixes. “It can be pretty material,” they explain. “If somebody’s used a version of something that has a vulnerability, and the fix is to upgrade to a version that has material changes, they might have to go and rewrite the entire codebase that they’ve written. It could add a good week or two onto something in the worst case scenario.” Even in simpler cases, the team faced “a fairly regular stream of like half-day, one-day fixes,” constant interruptions that pulled analysts away from building new models and strategic work.
By catching vulnerabilities proactively rather than reactively, Anaconda’s trusted distribution eliminated these cascading effects of dependency conflicts. “We need all the different code versions to work together.”
“It makes development much more secure for us, and because we have that confidence that what we’re going to release hasn’t got any vulnerabilities, it also removes human error,” they note. The seamless integration of Anaconda also provided immediate efficiency gains in daily workflows, with improvements that compound across the team’s development work to create meaningful productivity gains over time.
Accelerating Critical Fraud Detection
With the Anaconda Platform providing a secure development environment, the bank’s data science team could focus on building sophisticated fraud detection models that protect both the bank and its customers from financial crime.
Fraud detection in banking requires analyzing vast amounts of disparate data in real-time, including transaction details and customer spending history, behavioral patterns, device information, and session data. The challenge is detecting fraudulent activity among the less than 1% of transactions that are actually fraudulent, while minimizing false positives that would disrupt legitimate customers.
“The only way to do that is to use data science,” the Head of Data Science explains. “You need to find that balance between making the customer experience as smooth as possible, not declining legitimate transactions, and not having people sit at checkout ringing their bank to approve purchases.”
The business impact has been remarkable. Using machine learning models built on the Anaconda Platform, the bank has reduced fraud by over 90% in value and 96% in volume, with a very limited impact on genuine customers and complaints.
For small businesses, a core part of the bank’s customer base, this protection is particularly crucial. “Some of the most damaging fraud for small businesses is scams such as invoice redirect or CEO scams as well as account takeover,” they note. “Small businesses also sometimes have heightened risk of card fraud in instances where they may have multiple cardholders within the company, which increases the risk of the details being compromised.”
Enabling Enterprise-Scale AI Innovation
What sets Anaconda apart for the challenger bank is its comprehensive approach to the entire AI development lifecycle. Rather than piecing together multiple security tools, they leverage a unified platform that addresses their needs from development through deployment.
“What we get is all the benefits of open source: newer things are available very quickly, there’s a huge amount of innovation happening,” the Head of Data Science explains. “Anaconda overlays the most important elements of enterprise security onto an open-source language, which means you retain access to cutting-edge tools while bringing in the security and governance elements that we need as a bank.”
For financial crime detection, where the team might need to implement new algorithms or retrain existing models quickly in response to emerging threats, this streamlined workflow is essential. “You do sometimes need to react quite quickly. You might want to implement a new algorithm or retrain an existing one and get it into production as quickly as you can,” they note. “That can only be done if you make the process as simple as possible.”
Expanding AI Across the Organization
The success with fraud detection has opened doors for broader AI initiatives across the bank. The team is now using Anaconda across all their data science projects, with key initiatives including simplifying their account underwriting process and implementing more advanced machine learning in credit, customer experience, and marketing areas.
“We have been able to use more advanced ML in areas such as credit, customer experience, and marketing where before we were limited in what we could automate,” the Head of Data Science explains.
The Governance Advantage
For the bank’s security team, Anaconda provided exactly what they needed: a trusted solution that removed human error from the security equation while enabling innovation.
“Our CISO was involved in the sign-off process,” the Head of Data Science recalls. “They were thrilled when I suggested it as a tool, because otherwise it would have been something we had to build manually. We would have had to basically do what Anaconda has done ourselves, which wouldn’t have been a valuable use of time.”
With Anaconda’s governance framework in place, the organization gained the visibility and control needed to satisfy audit requirements while empowering the data science team to innovate at speed. This eliminated the traditional tension between security and agility.
Looking Forward: The AI-First Future
As the bank continues to expand their AI capabilities, they’re positioned to unlock even greater efficiencies with Anaconda as their foundation. The team’s roadmap includes further automation of underwriting processes and continued expansion of machine learning applications across customer-facing and operational systems.
“The most important part of the process is how you are bringing AI/ML into production in a secure, efficient, and scalable manner,” the Head of Data Science reflects. “Anaconda has been very important in allowing us to ensure we have security embedded into our code from the onset of a project, making the transition to production much smoother.”
Their advice to other data science leaders emphasizes minimizing the gap between development and production environments: “I would recommend ensuring that the tools you use in development of ML/AI are as closely aligned to those you use to bring it into production as possible, in order to minimize the amount of re-coding involved in implementation. The smaller the gap between these two environments, the faster the implementation and the lower the chance of models being implemented incorrectly.”
For this challenger bank, the Anaconda Platform has delivered exactly what they needed: the confidence to innovate with open source at enterprise scale, backed by the security and governance their business requires. From protecting customers against sophisticated fraud schemes to enabling rapid deployment of new AI capabilities, Anaconda has become the foundation for their data-driven transformation.
Ready to accelerate your AI initiatives with trusted open source? Discover how the Anaconda Platform can transform your data science workflows while maintaining enterprise-grade security and governance. Contact our team for a personalized demonstration.
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