The Problem We Were Actually Solving
Our AML and KYC filters were designed to prevent us from facilitating illegal activities like money laundering and terrorist financing. The issue was that the filters were overly aggressive, blocking legitimate transactions from sellers in certain countries due to outdated or overly broad sanctions lists. This made it difficult for our platform to attract sellers from emerging markets, which were precisely the countries where our platform could make the most impact.
What We Tried First (And Why It Failed)
We initially tried to address this issue by whitelisting certain countries or adding manual review processes. However, these solutions were complicated to implement and often created more problems than they solved. For example, whitelisting countries would create new security risks if not properly managed, while manual review processes would introduce delays and increased operational costs. Neither solution was scalable or sustainable in the long run.
The Architecture Decision
After months of research and collaboration with experts from the financial industry, we implemented a risk-based approach to AML and KYC filtering. This involved using machine learning algorithms to analyze transaction patterns and assess the risk of each transaction in real-time. We also implemented a tiered filtering system, which allowed us to balance the risk of blocking legitimate transactions with the risk of allowing illicit activities to go undetected.
What The Numbers Said After
The results were staggering. By implementing the risk-based approach, we reduced the false positive rate of our AML and KYC filters by 75%, allowing us to onboard more sellers from restricted countries without increasing the risk of facilitating illicit activities. Our platform saw a significant increase in sales from emerging markets, with a corresponding growth in revenue and user base.
What I Would Do Differently
If I were to do this project again, I would prioritize building a data-driven culture from the outset. By incorporating more robust data analytics and machine learning capabilities into our AML and KYC filtering system, we could have accelerated our development and iteration cycles, reducing the time it took to achieve the desired results. I would also focus on more granular risk assessment, taking into account factors like the seller's business model, industry, and transaction history to more accurately assess their risk profile.
This project taught me a valuable lesson about the importance of balancing security and compliance with business needs. By taking a risk-based approach to AML and KYC filtering, we were able to create a more inclusive platform that could accommodate sellers from restricted countries without compromising our security and compliance obligations. As an engineer, it's this type of nuanced thinking that really allows you to make a difference in the lives of your users.
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