With these objectives in mind, Faktion was brought on board to accomplish two primary goals:
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Implement a robust MLOps infrastructure that would streamline and fine-tune AI application development.
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Deliver a first use case focused on churn prediction, helping the bank proactively identify and retain valuable customers.
By prioritizing 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.
Challenges: Bridging the Gap Between Technical Tools and Business Teams
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:
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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 modernize its operations but also improve how it serves its customers—every step of the way.
Solution: Driving Customer Retention with Scalable MLOps and Actionable AI Insights
An external AI partner was engaged to design and implement a comprehensive MLOps setup in Azure Cloud. This engagement covered multiple key components:
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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.
Bringing AI to Business Users
To bridge the gap between technical AI tools and business users, Faktion designed intuitive interfaces and workflows that democratized 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 prioritize 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:
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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, prioritizing 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 personalization.
Outcome: 99.3% Churn Prediction Accuracy & Scaling AI Applications
The newly implemented AI solution is expected to deliver measurable benefits for the bank:
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Pinpoint Accuracy in Resource Allocation
By leveraging the churn prediction model, the bank achieved 99.3% precision in identifying the top 0.1% of customers most likely to churn. This ensures retention efforts are highly targeted, saving resources and maximizing impact. -
Significant Churn Reduction with Minimal Effort
Focusing on just the top 2% of customers allowed the bank to address nearly 30% of potential churn, delivering substantial business impact while maintaining efficiency in outreach campaigns. -
Scaling to Future AI Applications
With the MLOps framework fully established, the bank can efficiently develop, deploy, and manage multiple new AI applications at scale.
In addition, Business users now have powerful, intuitive dashboards at their disposal. These dashboards not only enable them to interact with the churn prediction model directly but also provide clear insights into churn probabilities and patterns. This empowers business teams to make data-driven decisions faster and align interventions with strategic goals.
Faktion’s collaborative and innovative approach not only addressed the technical challenges but also ensured that AI became a tool for everyone, helping the bank achieve its goals and pave the way for future growth.