Transforming Fraud Detection: Radial’s Migration to Amazon SageMaker
A Collaborative Approach to Modernizing Machine Learning Workflows
Overcoming Challenges in On-Premises ML Models
The Impact of AWS Experience-Based Acceleration on Radial’s Journey
Streamlining ML Development: A Comparison of Legacy and Modern Workflows
Enhancing Security and Compliance in Fraud Detection
The Multifaceted Benefits of AWS-Enabled Machine Learning
Key Lessons from Radial’s Migration Experience
Conclusion: A New Era in Fraud Detection and E-commerce Solutions
Transforming Fraud Detection: Radial’s Journey to Modernizing ML Workflows with AWS SageMaker
In the fast-paced world of eCommerce, fraud detection is not just an operational necessity but a critical component to ensuring brand integrity and consumer trust. This blog post, co-written with Qing Chen and Mark Sinclair from Radial, dives into how Radial, a leading player in the fulfillment landscape, enhanced its fraud detection capabilities through the modernization of its machine learning (ML) workflow using AWS SageMaker.
About Radial
Radial is the largest third-party logistics (3PL) fulfillment provider, offering integrated payment, fraud detection, and omnichannel solutions tailored to mid-market and enterprise brands. With over 30 years of industry experience, Radial is committed to helping brands navigate common eCommerce challenges, enabling them to deliver a seamless and secure shopping experience from click to delivery.
In today’s digital landscape, strong fraud detection systems are essential for businesses aiming to protect their operations while ensuring customer satisfaction. Radial recognized the need for advanced solutions and turned to machine learning, thus embarking on a transformative journey to modernize its fraud detection systems.
The Need for Advanced Fraud Detection Models
Traditional approaches to fraud detection often fall short in adapting to the rapidly evolving tactics used by fraudsters. Here, machine learning (ML) significantly outperforms conventional methods, leveraging algorithms capable of analyzing vast amounts of transactional data to identify patterns and anomalies in real time. Continuous learning makes ML models not only resilient against evolving threats but also effective in reducing false positives over time. By migrating their fraud detection ML workflows to AWS SageMaker, Radial aimed to enhance efficiency, scalability, and long-term maintainability.
Challenges of On-Premises ML Models
Despite its benefits, managing ML models on-premises presents various challenges, particularly in scalability and maintenance.
Scalability
The physical hardware constraints of on-premises infrastructure limit the ability to process high transaction volumes, especially during peak shopping seasons. The slow and resource-intensive process of scaling can result in delayed fraud detection, potentially exposing brands to higher risks.
Maintenance
The manual effort required in maintaining servers and ensuring uptime can divert IT teams’ focus from more critical tasks. Additionally, the lack of automation tools leads to complexity and higher error rates in managing model updates and monitoring.
Common Modernization Challenges in Cloud Migration
Transitioning to cloud-based ML demands overcoming significant hurdles. Skill gaps in advanced technologies, cross-functional barriers, slow decision-making, and project management complexities can stall modernization efforts. Luckily, AWS’s Experience-Based Acceleration (EBA) program offers a structured approach to align customer goals, expedite cloud migration, and optimize ML workflows.
EBA: Empowering Collaboration for Transformation
The EBA program involves a structured 3-day workshop where participants get hands-on experience with SageMaker, addressing business goals and structuring ML problem frames. For Radial, EBA provided tailored advice in overcoming obstacles previously rooted in their on-premises infrastructure.
From Legacy to Modern Workflows: A Case Study of Radial’s Evolution
Radial’s legacy ML workflow faced notable bottlenecks, often taking weeks to develop, test, and deploy fraud detection models. By migrating to SageMaker, they transformed their workflows into a streamlined process:
Legacy Workflow:
- Model Development: Took 2-4 weeks due to limited on-premises computational resources.
- Deployment: Required extensive back-and-forth communication between data scientists and developers, resulting in several weeks for deployment.
Modern Workflow:
The adoption of MLOps with SageMaker vastly enhanced Radial’s operations:
- Faster Development: With on-demand computational resources, the data science team could execute more experiments, improving model performance and reducing development time.
- Seamless Deployment: The MLOps pipeline automated deployment processes, reducing deployment times from weeks to mere minutes, with high consistency across environments.
Building a Secure, Scalable MLOps Architecture
Radial’s MLOps architecture leverages key AWS services to facilitate a secure and efficient CL lifecycle from model development to production deployment. The incorporation of CI/CD pipelines, GitLab, and Terraform solidifies operational efficiency and enables streamlined collaboration across teams.
Key Features of the New Architecture:
- Dynamic Scalability: Rapidly adapts to shifting demands during peak times.
- Infrastructure as Code (IaC): Ensures consistency and reduces manual error during deployments.
- Robust Monitoring: Built-in tools for tracking model performance keep fraud detection systems robust and responsive.
Security and Compliance
Radial prioritizes the security of customer data, applying strong measures to maintain compliance with regulatory requirements such as CPPA and PCI. Their architecture incorporates secure practices like AWS Direct Connect for data in transit, Amazon VPC for isolating workloads, and AWS KMS for encrypting data at rest.
Key Benefits of the New Workflow
Radial’s transition to AWS has resulted in numerous advantages:
- Dynamic Scalability: Ensures readiness during peak traffic, keeping operational stability intact.
- Faster Infrastructure Provisioning: Dramatically shortens the model deployment cycle.
- Consistency: Reduces the communication overhead between data science and engineering teams.
Lessons Learned from Radial’s Modernization Journey
Radial’s experience offers valuable insights for other organizations looking to modernize their MLOps workflows:
- Engage AWS: Collaborate with AWS for tailored solutions and to adapt templates to specific needs.
- Iterative Customization: Work closely with internal teams and AWS Support throughout the customization process.
- Account Isolation for Security: Separate development, testing, and production environments while ensuring collaboration.
- Load Testing: Regularly fine-tune scaling strategies based on thorough load testing to prepare for high-demand periods.
Conclusion
The story of Radial showcases the critical role of modernizing ML workflows in fraud detection to maintain operational excellence in eCommerce. Through their partnership with AWS, Radial effectively reduced ML model deployment cycles by over 75% while enhancing overall model performance by 9%. These advancements empower Radial to swiftly respond to evolving fraud threats while ensuring a secure and reliable experience for its customers.
“In the ecommerce retail space, mitigating fraudulent transactions and enhancing consumer experiences are top priorities for merchants. High-performing machine learning models have become invaluable tools in achieving these goals.”
— Lan Zhang, Head of Data Science and Advanced Analytics, Radial
For organizations looking to harness the power of cloud-based ML, exploring AWS’s EBA program could be your first step in a successful migration journey toward enhancing fraud detection and operational resilience.
By sharing this success story, we hope to inspire other businesses in the eCommerce space to embrace cloud technologies and modern ML workflows to protect their brands and foster consumer trust.