GDAC AML: Building the Data Foundation for Compliance

At GDAC Exchange, I built and upgraded systems supporting anti-money-laundering operations. The project connected customer, trading, deposit, and withdrawal data to an AML solution and integrated KYC, risk assessment, watch-list filtering, and fraud-detection functions into one exchange workflow.

Project context

AML operations at a virtual-asset service provider do not end in a single system. Know Your Customer (KYC), risk assessment (RA), watch-list filtering (WLF), and fraud detection (FDS) must be connected, while customer and transaction data must arrive under consistent identifiers and timing. Missing or mismatched data can undermine every later risk decision.

The exchange’s existing data architecture also had to evolve as regulatory requirements and the AML solution changed. We needed to modify the data model and integration without compromising the stability of a live service.

My role

• Software engineer at Peertec/GDAC Exchange
• AML data ETL and system integration
• Participation in database-schema redesign
• Requirements and milestone coordination

Period: January–August 2024

What I worked on

I developed Python ETL programs that delivered customer, transaction, deposit, and withdrawal data into AML workflows. The programs normalized information from source systems, checked required fields and formats, and preserved consistent outcomes under repeated or delayed processing.

I also contributed to redesigning exchange database schemas for new compliance requirements and solution integrations. We compared the operational data model with what AML functions required, evaluated the impact on existing services, and considered migration and validation as part of the design.

KYC, RA, WLF, and FDS were integrated so that customer information could flow through risk assessment, monitoring, and follow-up action. We verified that one component’s output retained the same meaning in the next, rather than treating each integration as an isolated feature. I also helped organize regulatory requirements and milestones so that the participating teams worked from a shared standard.

What I learned

The quality of an AML system is often determined by data consistency rather than its most visible feature. A sophisticated detection function is of little value if customers and transactions are joined incorrectly. ETL was not merely data movement; it was the work of making different systems interpret the same fact in the same way.

Compliance was also neither solely an engineering project nor solely a regulatory one. People who understood the legal obligation, the source data, and the operating system had to align continuously. I learned that implementing a requirement matters alongside connecting the language of each stakeholder and preserving a result that can later be verified.

Technology and link

• Backend and ETL: Python
• Database: MySQL
• GDAC Exchange: gdac.com

Technologies: Python, MySQL, AML, KYC, compliance engineering