In this critical project, we spearheaded the complete migration of Tipico’s AML data science workflows from the legacy KNIME platform to a more powerful and flexible Python-based big data analytics environment. This transformation was designed to significantly improve the scalability, performance, and automation of their anti-money laundering (AML) systems, essential for staying ahead in the fast-evolving financial compliance landscape.
By harnessing the capabilities of AWS Airflow, we automated and orchestrated complex ETL (Extract, Transform, Load) pipelines, enabling seamless integration with Tipico’s centralized data warehouse and facilitating real-time access to critical compliance data. Alongside this migration, we continued to maintain and optimize Tipico’s AML risk-ranking model, which assesses users based on money laundering risk, ensuring it remains precise and effective in identifying suspicious activity. This upgrade empowered Tipico with advanced big data analytics tools and workflow automation, enhancing their ability to detect fraud, reduce operational risk, and meet stringent regulatory requirements with confidence.
Migrated KNIME AML workflows to Python for big data analytics
Automated ETL pipelines using AWS Airflow
Integrated workflows with Tipico’s data warehouse
Enhanced AML risk-ranking model for better accuracy
Automated processes to reduce errors and boost efficiency
Our solution seamlessly combined cloud-native orchestration, advanced data engineering, and big data analyticstechnologies to build a scalable, efficient AML platform tailored to Tipico’s compliance needs. Migrating to Python provided greater agility in developing and updating AML models, while AWS Airflow empowered automated and fault-tolerant scheduling of complex data workflows. This modernized infrastructure enables Tipico to proactively detect and manage money laundering risks while ensuring full compliance with international anti-money laundering laws.
Our migration to Python and automation with AWS Airflow has revolutionized AML compliance, delivering faster insights and greater accuracy while future-proofing our data workflows for Malta’s evolving regulatory landscape.
Matthew Galea - Managing Director
This case highlights how migrating AML workflows to Python and leveraging cloud orchestration tools like AWS Airflow can revolutionize big data analytics in financial compliance. Tipico now benefits from a highly efficient, reliable, and scalable AML platform that enhances fraud detection and ensures regulatory adherence in an ever-changing compliance landscape.
- Scalable Python workflows handling large, complex datasets
Improved AML risk-ranking accuracy for detecting high-risk users
Fully automated ETL orchestration reducing manual errors
Real-time data warehouse integration for up-to-date AML insights
Enhanced compliance with global AML regulations via advanced analytics
Future-proof infrastructure supporting ongoing regulatory requirements
