A leading European bank accelerates the deployment of ML models from weeks to hours with an enterprise-grade, automated Databricks Analytics Platform.
About Company
A prominent European universal bank operating in the Polish market for over three decades, providing services across retail banking, corporate banking, agricultural finance, SME, and consumer lending. As part of a major international banking group with a global presence, the institution serves millions of customers through an extensive network of branches and partner locations.
The bank holds a significant position in the Polish banking sector and maintains a substantial market share in consumer loans. With thousands of employees, the bank delivers comprehensive financial services through both traditional branch networks and modern digital channels, including mobile banking and online platforms.
Services and Technology
Challenge
The bank faced critical limitations with its legacy analytical infrastructure, which hindered its ability to deliver personalized, data-driven banking experiences. The bank's existing Legacy Customer Intelligence Platform had become a significant bottleneck for digital transformation initiatives.
Legacy System Limitations:
- Outdated data infrastructure: The old data warehouse serving the existing solution was scheduled for decommissioning, requiring urgent migration to new data sources
- Scalability constraints: Propensity-to-buy machine learning models were too slow for real-time customer interactions, unable to handle the bank's growing customer base of 2.5 million
- Limited algorithmic capabilities: The closed Legacy Customer Intelligence Platform environment prevented Data Scientists from accessing modern ML algorithms, restricting innovation in predictive analytics
- Insufficient processing power: Computing limitations made it impossible to deliver near real-time predictions and feed content to mobile applications
- Architectural inflexibility: The system couldn't rapidly adapt to new data sources appearing in the organization, limiting agility in product development
Business Impact: The legacy platform hindered the bank's ability to implement sophisticated customer personalization strategies across its digital channels. With customer expectations rising for personalized banking experiences, the bank needed to implement advanced analytics for product recommendations, communication channel optimization, and real-time customer engagement - capabilities that were impossible with their existing infrastructure.
Solution
Modern Analytics Platform Implementation: Elitmind designed and implemented a comprehensive cloud analytical platform utilizing Microsoft Azure architecture, adhering to the Cloud Adoption Framework (CAF) and leveraging Databricks technology. The solution created a unified, scalable data ecosystem capable of processing vast amounts of customer data, turning it into Features and then ready-to-use ML models.
Core Technology Stack:
- Azure Databricks: Databricks provides a unified analytical platform serving users across the entire organization, enabling data-driven decision-making at every level
- MLflow Integration: MLflow Model Registry integrated with Unity Catalog provides centralized governance for models with unified model versioning, dependency management, validation, and governance
- Elitmind Proprietary Solutions: Custom-built Ingestion Framework, Feature Store, and Model Factory explicitly designed for banking use cases.
Advanced MLOps Infrastructure The platform implements a complete MLOps framework, enabling rapid model development and deployment. Data Scientists can now develop and deploy models within a single environment using predefined extensive feature sets, completing the entire process in as little as one day compared to the previous weeks-long timeline.
Enterprise-Grade Security and Compliance: A full ISO 27001 configuration ensures the platform meets stringent banking security requirements while maintaining operational efficiency. The solution provides automated model and feature monitoring with minimal analyst effort, ensuring consistent performance across all deployed models.
Comprehensive Analytics Capabilities The platform enables the bank to solve complex business challenges, including product selection optimization, personalized benefits in loyalty programs, communication channel optimization, sequential event management, and real-time message customization for digital channels.
Results
The implementation delivered transformative results, positioning the bank as a leader in data-driven banking innovation. The new analytical platform fundamentally changed how the bank approaches customer analytics and model deployment.
Operational Excellence:
- Rapid Model Deployment: Model development and deployment reduced from weeks to hours, enabling rapid response to market changes
- Dynamic Scalability: Computing resources now dynamically scale for Customer 360 calculations, handling peak loads efficiently
- Unified Data Architecture: Single source of reliable customer information eliminates data silos and inconsistencies
- Cost-Effective ML Operations: ML teams can now use highly scalable Databricks clusters cost-effectively with automatic resource optimization
Advanced Analytics Capabilities:
- Near Real-Time Personalization: The platform is ready to enable near real-time customer content personalization across mobile and digital banking channels
- Predictive Banking: Advanced propensity models now power product recommendations and customer lifecycle management
- Omnichannel Integration: Seamless integration with external Campaign & Interact system and credit management application
- Automated Quality Assurance: Comprehensive model monitoring ensures consistent performance with minimal manual intervention
Tailored for success
"I confirm the completion of the New Analytical Platform project. The creation of the Analytical Platform helps solve critical business challenges, including selecting the most suitable banking product, offer, or pricing for customers and optimising communication channels to enhance the customer experience through improved channel usage and communication sequences. The platform enables our Data Scientists to develop and deploy models within a single environment, with the entire process completed in as little as one day."
— Executive Director CRM, Customer Intelligence and Data Science, Leading European Bank
Our Approach
Elitmind conducted a comprehensive evaluation of the bank's outdated Legacy Customer Intelligence Platform, identifying critical bottlenecks preventing digital transformation. Rather than incremental fixes, the team recognized the legacy data warehouse's decommissioning as an opportunity for complete modernization using Microsoft Azure cloud infrastructure, transforming weeks-long model deployment into same-day operations.
The team architected a comprehensive cloud analytical platform using Microsoft Azure and Cloud Adoption Framework (CAF) principles. Azure Databricks served as the unified analytical platform, providing dynamic scalability for Customer 360 calculations while optimising costs through automatic resource management and leveraging Azure's native scaling capabilities.
A comprehensive MLOps infrastructure was implemented using MLflow Model Registry with Databricks Unity Catalog for centralized governance and model versioning. This Databricks-native framework enabled data scientists to work within a single environment with predefined feature sets, reducing development time from weeks to hours through automated Azure deployment pipelines
Elitmind developed proprietary solutions, including custom-built Ingestion Framework, Feature Store, and Model Factory specifically for banking use cases, all built on the Databricks platform. These components leveraged Azure's data processing capabilities to handle unique financial data requirements, enabling complex use cases such as product selection optimisation and personalized loyalty programs.
The platform implemented a comprehensive ISO 27001 configuration, leveraging Microsoft Azure's enterprise security features to meet stringent banking security requirements while maintaining operational efficiency. Databricks' built-in security controls and automated monitoring ensured consistent performance across deployed models with minimal analyst effort.
The solution seamlessly integrated with existing systems, including Campaign & Interact and credit management applications, using Azure's integration services and Databricks' real-time processing capabilities. This unified Microsoft-powered architecture eliminated data silos, enabling near real-time personalization across mobile and digital banking channels.