AML Transaction Monitoring System

The Challenge

A government agency responsible for AML oversight needed a system to process and analyze bank statements at scale, identifying transaction details and flagging suspicious patterns. The challenge was compounded by the bilingual nature of Maltese financial documents, inconsistent formatting across different banks, and the sheer volume of transactions requiring review. Manual analysis was slow, inconsistent, and unable to keep pace with growing regulatory demands.

Our Approach

Neural AI built a comprehensive AML transaction monitoring system:

• Intelligent document processing automatically extracting transaction details, account information, and metadata from bank statements in various formats (PDF, scanned images, CSV)
• Maltese language NLP handling bilingual documents that mix Maltese and English text, ensuring accurate extraction regardless of language
• Transaction comparison engine cross-referencing transactions against known patterns, blacklists, and behavioral profiles to identify suspicious activity
• Structured data output converting raw document data into organized spreadsheets and database records for downstream analysis and Power BI reporting
• Explainable flagging providing clear reasoning for each suspicious transaction alert, supporting compliance officers in their review decisions
• Comprehensive summarisation generating case summaries and regulatory reports from analyzed transaction data