AML Enhanced with AI and Automated Workflows

The Challenge

Tipico, a leading iGaming operator, relied on KNIME-based workflows for their Anti-Money Laundering (AML) data science operations. These workflows were becoming increasingly difficult to scale, maintain, and integrate with their growing data infrastructure. The manual ETL processes were slow and prone to errors, while the existing AML risk-ranking model needed optimization to handle higher transaction volumes and evolving regulatory requirements.

Our Approach

Neural AI executed a comprehensive migration and optimization project:

• Migrated KNIME AML workflows to Python creating more maintainable, version-controlled, and scalable data science pipelines
• Automated ETL pipelines using AWS Airflow enabling scheduled, monitored, and fault-tolerant data processing with full orchestration
• Integrated workflows with Tipico’s data warehouse ensuring seamless data flow between AML systems and the broader data infrastructure
• Enhanced the AML risk-ranking model improving accuracy through advanced feature engineering, model retraining, and performance benchmarking against regulatory standards