Getting 90% Churn Prediction in Banking within a New MLOps Environment

A well-known Belgian Bank serving over a million customers embarked on a large-scale digital transformation to enhance its services and maintain high customer satisfaction. Preparing the infrastructure and implementing the first AI use case was an important element of this transition. One of the near-term pivotal objectives of this client was ensuring long-term customer engagement and minimising churn. With these objectives in mind, Faktion was brought on board to accomplish two primary goals: Implement a robust MLOps infrastructure that would streamline and fine-tune AI application development & deliver a first use case focused on churn prediction, helping the bank proactively identify and retain valuable customers. By prioritising innovation and leveraging advanced cloud technologies, the bank aimed to reinforce its position in a highly competitive market while maintaining strict regulatory and security standards.

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Need

Challenges

One of the biggest barriers to AI adoption was that most AI applications were designed with data scientists and technical experts in mind. This left business users—those who would benefit most from AI insights—struggling to use or adjust these models effectively. The bank needed a solution that made AI accessible to all stakeholders, empowering business teams to extract value without relying entirely on technical specialists.

In addition, this financial institution faced several hurdles as it embarked on its digital transformation journey, especially when it came to making AI a central part of its operations. Here’s what they were up against:

Building a Secure and Compliant AI Infrastructure
When dealing with sensitive customer data, you can’t cut corners. The bank needed a solid MLOps framework that wasn’t just about building and running AI models but also about keeping them secure, scalable, and fully compliant with strict banking regulations.
Spotting and Preventing Customer Churn
With over a million customers, keeping everyone happy is no small task. The bank wanted a way to quickly identify customers at risk of leaving and take steps to keep them on board—especially their most valuable clients.
Upskilling Internal Teams
The bank’s team was experienced, but AI and MLOps were new territories. To make the most of their investment, they needed guidance and hands-on training to manage these systems and drive future AI projects confidently.

By tackling these challenges head-on, the bank aimed to not only modernise its operations but also improve how it serves its customers—every step of the way.

The solution

Solution

An external AI partner was engaged to design and implement a comprehensive MLOps setup in Azure Cloud. This engagement covered multiple key components:

MLOps Framework Implementation
The AI partner built a robust environment leveraging Azure’s native capabilities to ensure security and compliance. With this framework in place, the institution could develop, deploy, and monitor AI models that are both scalable and regulation-compliant.
Churn Prediction Model
The partner developed a custom churn prediction model tailored to the institution’s unique data. Using insights from over a million customer records, the model identifies at-risk clients—especially high-value ones—so the bank can take swift, proactive measures to retain them.
Empowering Internal Teams
To ensure long-term success and autonomy, the AI partner provided extensive training on MLOps best practices. This included hands-on guidance for pipeline development, release management, reproducibility, and thorough documentation to support future AI use cases.

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Bringing AI to Business Users

To bridge the gap between technical AI tools and business users, Faktion designed intuitive interfaces and workflows that democratised AI capabilities. These interfaces provided actionable insights for account managers and other non-technical users, including detailed churn probabilities for individual customers and segments, making it easy for business teams to identify trends and prioritise retention strategies. With these tools, business users could not only interpret results but also adjust parameters and implement informed actions without requiring a data science background.

Approach

The external AI partner followed a structured and collaborative process, aligning every step with the institution’s strategic objectives:

Assessment and Planning
The initial phase involved a complete review of the bank’s IT environment, data architectures, technology stacks, and existing AI capabilities. Based on these findings, the team developed an in-depth plan to address challenges and map out the MLOps framework.
Implementation
During this phase, the MLOps framework was built within Azure Cloud, prioritising security and scalability to align with regulatory demands. The churn prediction model was then developed and deployed, making use of the bank’s integrated datasets.
Validation and Expansion
Once operational, the churn model was validated against real-world scenarios to confirm its accuracy and reliability. After any necessary refinements, the next phase involved exploring additional AI-driven functionalities—such as a recommendation system for best-next-action strategies—further enhancing customer retention and personalisation.

Outcome

Over 90% Churn Prediction Accuracy & Scaling AI Applications

The implementation of a comprehensive AI solution marked a major step forward in the bank’s digital transformation journey. By combining advanced churn prediction techniques with a robust and scalable MLOps infrastructure, the bank successfully empowered business users and significantly enhanced customer retention capabilities.

Enhanced Churn Prediction Capability: The implemented AI solution enabled the bank to achieve precision rates above 90%, effectively identifying high-risk customers early and allowing proactive retention measures.
Efficient Customer Retention Efforts: By focusing retention strategies on a small, targeted customer segment, the bank successfully addressed nearly one-third of potential churn risk, significantly boosting retention while optimising outreach efficiency.
Future-Proof AI Scalability: The established MLOps framework provides a robust foundation for efficiently scaling and managing future AI applications, ensuring continuous innovation and regulatory compliance.

In summary, the collaboration not only met immediate business objectives but also prepared the bank to confidently scale AI-driven strategies, ultimately enhancing customer satisfaction and positioning the organisation strongly for future growth.