Banking on AI: Implementing Compliant MLOps for Financial Institutions

If you're building machine learning models at a bank, you're probably feeling squeezed. On one side, fintech startups push updates to their ML systems multiple times a day, setting customer expectations sky-high. On the other, regulators worldwide—from the US Fed and Europe's EBA to Singapore's MAS—are tightening the rules around ML governance, requiring detailed documentation, rigorous testing, and comprehensive model explainability.

The challenge is clear: Can banks deploy machine learning quickly, securely, and still meet stringent compliance requirements? The answer is yes—but it requires the right approach. Let's unpack how.

A Global Shift Toward Stricter ML Governance

Banks across the globe face increasing scrutiny on their AI deployments:

• US: The Fed's SR 11-7 guidelines mandate clear, documented "effective challenges" for every ML model used in critical financial decisions.
• Europe: The EU's Digital Operational Resilience Act (DORA) and the AI Act require banks to maintain complete model lineage and demonstrate thorough testing and bias mitigation by early 2026.
• APAC: In Singapore, MAS's FEAT principles and Australia's CPS 230 put fairness, transparency, and operational resilience front and center.

No matter where your bank operates, chances are there's a ticking regulatory clock counting down your compliance deadline. The 2024 Banking AI Regulatory Outlook shows these requirements will only intensify in coming years.

Why Traditional Banks Lag Behind Fintech Challengers

Large incumbent banks often struggle to balance these new regulatory demands with the speed needed to remain competitive. While they have the resources and desire to deploy AI at scale, their legacy systems severely slow down model deployment cycles. This isn't typically due to a lack of ML talent or poor algorithms, but rather structural challenges that impede implementation. Legacy tech stacks, manual governance processes, and fragmented data systems create significant bottlenecks that make regulatory compliance particularly burdensome.

Most traditional banks operate on decades-old core systems designed for stability, not AI-era agility. Their data is typically spread across 10-15 different storage systems aligned to product lines or departments, making it nearly impossible to gather the comprehensive datasets modern ML requires while simultaneously maintaining the audit trails that regulators demand.

Organizational structures compound these technical challenges. Traditional banks often follow waterfall development practices rather than agile methodologies. Data scientists might build promising prototypes, but handing them off to separate IT teams for production deployment introduces delays and misalignments. Meanwhile, stringent governance requirements mean every model—especially those touching credit decisions or trading—must undergo extensive documentation, validation, and approval by multiple committees before going live.

By contrast, fintech challengers like Monzo, Nubank, and Revolut operate without this legacy baggage. They rely on cloud-native stacks, continuous integration pipelines, and fully automated governance checks. Their cross-functional teams own models end-to-end, eliminating handoffs between departments. For example, Monzo built its ML platform on cloud services with small teams embedded directly in business units like fraud and lending. Revolut's "Sherlock" fraud system retrains models nightly on the latest data, while N26 can deploy new services in a single hour. This lets them iterate on models and respond to customer and market needs rapidly, often deploying multiple times per day—a pace traditional banks can only aspire to match.

Five Essential MLOps Practices to Bridge the Gap

While incumbents can't transform overnight, they can begin closing the innovation gap by adopting MLOps practices incrementally. The journey to modern ML deployment requires patience, but starting with targeted improvements can deliver early wins while building toward comprehensive transformation.

Many leading banks are finding success with a phased approach:

1. Begin with Comprehensive Data Lineage: Start by mapping and documenting your existing data flows, then gradually introduce automated tracking and versioning tools for your most critical models. This creates immediate regulatory benefits while laying groundwork for future automation. Data lineage tools can be implemented model-by-model rather than requiring enterprise-wide deployment at once.
2. Introduce Validation Testing Incrementally: Rather than overhauling entire governance processes, introduce automated checks for single high-priority use cases first. Many banks start with fraud detection models where continuous validation provides both business and compliance value. Bias detection frameworks can be piloted in non-production environments before wider adoption.
3. Build Model Observability Capabilities Gradually: Begin monitoring your most business-critical models for concept drift and performance degradation. During COVID-19, 35% of banks reported negative model performance due to inability to quickly update models—focusing observability efforts on these vulnerable areas creates immediate value.
4. Develop LLMOps Capabilities Through Controlled Pilots: For banks exploring large language models, start with internal use cases and small-scale experiments. Integrate basic prompt management and hallucination detection in controlled environments before expanding to customer-facing applications.
5. Implement Resource Management Through IT Governance: Leverage existing IT governance frameworks to create initial controls over ML computing resources, ensuring budget compliance without requiring entirely new systems.

This gradual approach allows banks to achieve meaningful progress while respecting organizational constraints and existing governance structures.

Success Story: NatWest's MLOps Transformation

NatWest Group provides a compelling example of successful MLOps modernization. Facing lengthy deployment cycles, the UK bank built a scalable platform on AWS that:

• Reduction of idea-to-value time for data and analytics use cases from 40 to 16 weeks
• Cut environment creation time from 35-40 days to just 1-2 days
• Standardization of the ML model development and deployment process across multiple teams

This transformation enabled them to rapidly deploy AI models that adapt to changing customer behavior patterns—proving traditional banks can achieve fintech-like agility without compromising governance. It must be noted however that this transition nearly a year, and is still not fully complete.

Putting It into Action: A Real-World MLOps Template For Banks

To help you start quickly, we've built an open-source ZenML pipeline—the Bank Subscription Prediction project—that showcases these practices in action. This example pipeline:

• Automatically manages data lineage and versioning.
• Includes built-in bias and performance tests that run automatically with every deployment.
• Deploys seamlessly across various infrastructures (cloud, on-premise, hybrid) with minimal changes.

With ZenML, the pipeline itself generates all necessary compliance documentation, freeing your engineers to focus on building models rather than writing audit reports.

Building Your MLOps Roadmap

While implementation timelines vary significantly based on organizational complexity and leadership support, a structured approach can help banks make steady progress. With proper executive sponsorship and cross-functional buy-in, an MLOps transformation could advance through these key phases:

Initial Assessment Phase:

• Audit existing models and identify immediate regulatory gaps
• Map current model governance workflows to identify bottlenecks
• Secure sponsorship from both technology and business leadership
• Identify one high-value use case with reasonable implementation complexity

Foundation Building Phase:

• Starting with the Bank Subscription Prediction project template, implement basic training steps for your use-case
• Establish basic data lineage documentation for this specific workflow using the ZenML model control plane
• Design and implement preliminary validation tests
• Engage compliance and risk teams early to align on governance requirements

Expansion Phase:

• Deploy your first ZenML pipeline for your pilot use case on a stack of your choosing
• Validate that automated lineage and observability meet compliance requirements
• Document success metrics and lessons learned
• Develop a scaled implementation plan based on initial results

For most banks, this progression unfolds over 6-12 months rather than weeks, with organizational alignment often taking as long as the technical implementation. The key is starting with manageable initiatives that demonstrate value while building toward more comprehensive capabilities.

Looking Ahead: The Converging Future of Banking MLOps

The future of banking MLOps likely involves convergence—traditional banks becoming more agile and tech-driven through digital transformation initiatives, while fintechs mature and implement more robust oversight as they scale. Both will leverage MLOps to balance innovation with trust, with the competitive edge going to those who can deploy cutting-edge models swiftly, but with proper controls.

By 2026, when the EU AI Act fully takes effect, banks that have invested in modern MLOps will enjoy a substantial competitive advantage—able to meet compliance requirements through automation rather than manual processes, while continuing to innovate at the pace customers now expect. The future of AI in banking will belong to those who master this balance.

Key Takeaways

• Compliance and speed aren't mutually exclusive. Modern MLOps frameworks like ZenML make both achievable.
• Open, flexible solutions provide essential governance without compromising agility.
• Ready-to-use templates like the Bank Subscription Prediction project make implementation straightforward.

Banks no longer need to choose between fintech-style agility and regulatory compliance. With the right tools and practices, you can deliver both.

Ready to accelerate your bank's ML deployment while ensuring compliance? Book a demo with ZenML today to see how our MLOps platform can transform your AI initiatives and give you the best of both worlds.

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