From Bottleneck to Scale: dLocal's Compliance Automation Journey
Coverage of aws-ml-blog
The AWS Machine Learning Blog details how payment unicorn dLocal transitioned from a struggling proprietary AI tool to a scalable automation framework for high-volume merchant compliance.
In a recent case study, the aws-ml-blog discusses the operational challenges faced by dLocal, a cross-border payments unicorn, and their strategic pivot to automate compliance reviews. As fintech companies expand into emerging markets, the operational load of onboarding merchants while adhering to strict regulatory standards often outpaces human capacity. This post examines how dLocal utilized technology to resolve scalability issues inherent in their previous workflows.
The Compliance Scalability Trap
For a company like dLocal, which operates in over 40 countries, compliance is not merely a checkbox; it is a continuous, high-volume operation. The team is tasked with verifying the legitimacy of goods and services, validating domains, and monitoring for policy violations across thousands of new merchant websites every month. This involves cross-referencing product catalogs against a complex matrix of over 15 major categories and 100 subcategories of prohibited items.
The blog post highlights a critical industry signal: the limitations of early proprietary Generative AI solutions. In 2023, dLocal developed an internal GenAI tool to assist with this workload. However, the solution faced significant scalability hurdles. A substantial portion of cases still required manual intervention, creating a bottleneck that negated the efficiency gains the technology promised. This scenario reflects a broader trend where enterprise AI projects often stall at the "human-in-the-loop" phase, failing to achieve the reliability needed for full automation.
Automating the Review Process
The technical brief outlines dLocal's adoption of a solution referred to as "Amazon Quick Automate" to overcome these hurdles. By integrating this service, dLocal aimed to reduce the manual effort required to parse complex merchant inventories against their prohibited lists. The goal was to move beyond simple keyword matching to a more nuanced understanding of product listings, thereby minimizing human error and standardizing decision-making across different regions.
This transition is significant for engineering and product leaders in the fintech space. It demonstrates the shift from building bespoke, experimental AI models to leveraging managed infrastructure designed for production-grade workflows. The focus here is on the practical application of AI to solve a specific, high-value business problem-risk management-rather than the novelty of the technology itself.
Conclusion
For organizations struggling to scale their internal compliance tools or looking to understand the practical ROI of AI in risk management, this case study offers a clear example of operational refinement. It underscores the importance of moving from "working" prototypes to scalable architectures that genuinely reduce operational overhead.
Read the full post on the AWS Machine Learning Blog
Key Takeaways
- dLocal operates in 40+ emerging markets, requiring rigorous compliance checks for thousands of merchants monthly.
- A proprietary GenAI solution built in 2023 failed to scale, leaving a significant manual review burden.
- The compliance process involves checking against 100+ subcategories of prohibited products.
- Adopting the AWS-based solution allowed dLocal to reduce manual bottlenecks and minimize human error.
- The case study illustrates the challenges of scaling internal AI tools and the value of managed automation services.