Risk and compliance are more than just boxes to check. They define how much customers trust you, how regulators view you, and how efficiently you can operate. But with fast-changing threats and growing regulatory complexity, it's no longer sustainable to rely on manual or reactive processes.
Automation with machine learning helps organizations:
Amazon SageMaker provides a robust and scalable way to build, train, and deploy machine learning models without deep data science expertise. With SageMaker, teams can:
The result: real-time risk scoring, smarter compliance checks, and faster response to emerging threats.
Mactores partnered with Tilia to transform its risk and compliance operations by implementing a scalable, machine learning–driven pipeline using Amazon SageMaker. With growing transaction volumes and stringent regulatory requirements, Tilia needed to replace manual checks with intelligent automation.
Mactores enabled end-to-end model training, deployment, and monitoring to help Tilia detect fraud, streamline compliance, and meet PCI-DSS Level 4 standards, all while improving performance and reducing operational costs.
Tilia, a subsidiary of Linden Labs, is a financial services company powering virtual transactions across digital economies. With increasing volume and evolving regulations, Tilia needed a more innovative way to manage compliance and risk.
Mactores worked with Tilia to implement an ML-powered compliance and risk detection pipeline using AWS services, including Amazon SageMaker. The solution included:
Metric |
Before Implementation |
After Implementation |
Compliance |
Manual, error-prone processes |
Fully automated, PCI-DSS Level-4 certified |
Fraud Detection |
Reactive, rule-based checks |
Real-time ML-based anomaly detection |
Operational Efficiency |
High overhead, slow response times |
30% lower costs, 50% higher throughput |
Customer Experience |
Slower feature rollout, inconsistent UX |
40% faster time to market, 25% higher satisfaction |
Business Growth |
Limited by compliance bottlenecks |
20% increase in customer sign-ups |
Hurdle |
Solution |
Messy data |
Spend extra time cleaning and structuring it |
Lack of trust in AI |
Use automation as a decision-support tool |
Perceived cost |
Start in high-value/risk areas for quick ROI |
Automating risk and compliance isn't about replacing your team but empowering them. With Amazon SageMaker, you can catch risks earlier, enforce compliance faster, and let your experts do what they do best.
Tilia's success shows what's possible when automation is done right: improved security, happier customers, and stronger growth.
Whether you're exploring your first risk model or ready to scale an enterprise-wide compliance automation system, Mactores can help.
Amazon SageMaker enables businesses to build, train, and deploy machine learning models to analyze large volumes of transaction data in real time. These models detect anomalies, flag potential risks, and verify compliance automatically, reducing the need for manual reviews and speeding up decision-making.
Do I need a data science team to implement SageMaker for compliance automation?
No, you don't need a dedicated data science team to get started. Amazon SageMaker provides tools and managed services that simplify model development and deployment. Partnering with experts like Mactores can also help you quickly build and integrate effective models into your existing workflows.Businesses typically see faster fraud detection, fewer compliance breaches, reduced operational costs, and improved customer satisfaction. For example, Tilia achieved a 50% reduction in cyber threats and a 30% cut in operational costs after implementing an automated SageMaker solution.