Ensuring Compliance with AML/CTF Regulations in Remittance Transactions
One of the key projects I worked on involved developing a solution to filter remittance transactions against international and national blacklists to ensure compliance with Anti-Money Laundering (AML) and Counter-Terrorism Financing (CTF) regulations. This system needed to handle vast amounts of transaction data while identifying and preventing remittances linked to blacklisted users.
The Challenge
In the financial services industry, ensuring that transactions are not linked to individuals or organizations involved in illegal activities is critical. This project aimed to build a robust filtering system to stop any remittances that could be associated with entities flagged for AML/CTF concerns.
The system needed to handle two core challenges:
- Large Data Volumes: Transactions must be processed from a large dataset while ensuring minimal latency.
- Blacklist Management: Continuously cross-referencing users against international and national blacklists, including criminal data from multiple sources.
High-Level Design (HLD)
System Architecture
Technologies Used
To build this solution, we relied on the following tech stack:
- Apache Lucene: Used for indexing and searching through vast datasets efficiently.
- Java Spring Boot: Enabled the development of a scalable and maintainable backend system.
- Oracle Database: Managed and stored remittance data, as well as blacklist information for quick access during transaction filtering.
Data Sources
The project integrated data from several critical sources to ensure accurate blacklisting:
- DowJones Database: Provided international criminal data and lists of politically exposed persons (PEPs) relevant to AML/CTF regulations.
- Industry-Specific Restricted Databases: Included national and sector-specific blacklists, ensuring compliance with local regulations as well.
Filtering Process
Each transaction was checked in real time against the collected blacklist data to ensure that no prohibited remittance was processed. Here's how the filtering process worked:
- Data Indexing with Apache Lucene: The data from the blacklists was indexed using Apache Lucene to allow for high-speed querying. This made it possible to check transaction details against thousands of records efficiently.
- Transaction Validation: For each transaction, the system cross-referenced the sender and recipient against the indexed blacklist data. This included comparing details such as names, aliases, and other personal identifiers.
- Flagging & Aborting Transactions: If a match was found, the transaction would be flagged and aborted, and the system would log the details for compliance reporting.
Ensuring Compliance with AML/CTF Regulations
By implementing this transaction filtering system, the company could stay compliant with national and international AML/CTF regulations. The system not only prevented illegal transactions but also reduced the risk of fines and penalties associated with non-compliance.
Outcome
This project significantly enhanced the company’s ability to detect and block suspicious transactions. The solution ensured that large volumes of remittance data were processed quickly and efficiently while maintaining strict adherence to regulatory standards.
This project demonstrated the importance of creating a system that balances speed, accuracy, and regulatory compliance. Feel free to reach out if you’d like to discuss the technical details further!