INDUSTRY: Financial services industry
SOLUTION: Personalized customer experience, fraud detection
TECHNICAL USE CASE: Data ingest and ETL, machine learning
With over 1 million customers, Moneta is the 4th biggest bank in the Czech Republic and has repeatedly been recognized as the country’s most innovative bank. Databricks unified data analytics platform enables them to leverage data and advanced analytics to innovate the customer experience with use cases ranging from real-time recommendations to fraud detection.
Massive data volumes cripple legacy systems
As banks today collect more and more data across all their channels, Moneta found that their legacy analytical systems weren’t able to deal with the increase in data volumes. Moneta started a digital mission to improve their customer experiences with use cases such as real-time recommendations and fraud detection. Due to the massive volume of data at scale, it was challenging to move forward for Moneta to achieve their goals.
- Processing terabytes of data across all digital channels, making it extremely challenging to handle the large amount of data they had.
- Their on-premises solutions struggled to scale operations to support data science efforts against all of this data coming from various data sources at different speeds. As a result, time-to-insight was slower than they required to drive innovation.
- Data engineering spent too much time configuring clusters and maintaining infrastructure. This slowed data flow to the data science team for machine learning and analytics.
Simplified data engineering unlocks ML innovations
With the Databricks unified data analytics platform, Moneta can now tackle their big data and AI problems with a simple, scalable and secure managed service on AWS. It fosters a scalable and collaborative environment across data science and engineering, allowing data teams to more quickly innovate and deliver ML-powered innovations that feed into their services and products.
- Production ready and out-of-box solution for critical workloads.
- Sie wird vollständig verwaltet und beinhaltet ein automatisiertes Cluster-Management, wodurch die Verwaltung der Infrastruktur und der betrieblichen Abläufe – unabhängig von der Größenordnung – vereinfacht wird.
- Eine kollaborative Notebook-Umgebung mit mehreren unterstützten Sprachen (SQL, Scala, Python, R) ermöglicht es einem Team von unterschiedlichen Benutzern, in ihrer bevorzugten Sprache zusammenzuarbeiten.
Improving the banking experience with data and ML
With Databricks, Moneta has been able to harness the insights within their various streams of data to create innovative technologies that keep the world moving.
- Improved operational efficiency: Features such as auto-scaling clusters and support for Delta Lake and MLflow has unified analytics operations from data ingest to managing the entire machine learning lifecycle, cutting operational costs by two thirds.
- Better cross-team collaboration: Shared notebook environment with support for multiple languages has improved team productivity.
- Faster time-to-insight: Databricks has improved time-to-market, allowing them to accelerate prototype to production by 2X.
