NSUS Group Enhances Financial Fraud Prevention System with HAQM SageMaker

NSUS Group, a key player in the poker industry, operates a platform serving millions worldwide. Initially, it relied on Internet Data Centers (IDCs) for hosting, which met its needs but lacked scalability and efficiency. As the platform grew, NSUS Group saw an opportunity to strengthen its infrastructure, ensuring a seamless and secure gaming experience.

Compounding these challenges were the growing complexities of the business. NSUS needed its infrastructure to go beyond gaming to support advanced capabilities, such as user behavior analysis, and regulatory compliance. Financial fraud prevention became a critical priority as the company handled millions of daily financial transactions.

At the time, NSUS relied on manual investigative methods for fraud prevention. Skilled personnel analyzed historical data and user behavior logs, but this approach was resource-intensive, costly, and limited in scalability. 

Faced with these challenges, NSUS recognized the need for a more efficient and scalable solution. The company prioritized adopting automated ML pipelines to enhance fraud prevention capabilities, streamline operations, and reduce reliance on manual processes. However, transitioning to ML technologies was hindered by its legacy infrastructure and the high costs associated with implementing advanced systems.

Daniel Lim, chief product officer of NSUS Group, explained, “Previously, we relied heavily on human resources for our fraud prevention network. It became essential to adopt advanced technologies to scale our defense system and achieve greater efficiency.”

By addressing these challenges and focusing on innovation, NSUS set out to transform its operations, ensuring an even safer and more secure environment for its growing player base.

To address inefficiencies in its traditional anti-money laundering system, NSUS transitioned to a ML powered approach, building a predictive financial transaction management system. This system aimed to strengthen fraud prevention capabilities while streamlining operations. NSUS utilized HAQM Simple Storage Service (HAQM S3) to create a scalable, secure data lake for storing extensive user behavior data. To manage the machine learning (ML) workflow, NSUS adopted HAQM SageMaker for seamless data preprocessing, feature engineering, model training, hyperparameter optimization, and deployment.

During development, NSUS employed XGBoost-based training, a model widely used in e-commerce fraud prevention, and refined data with Apache Iceberg on AWS. By collaborating with AWS Partner Snowflake and adopting AWS Glue, NSUS streamlined data integration. 

HAQM SageMaker played a key role in optimizing hyperparameters, comparing models, tracking training history, and enabling collaborative model selection. Its testbed facilitated experimentation with diverse models and datasets, eliminating the need for specialized MLOps expertise. Additionally, NSUS used HAQM SageMaker Pipelines to manage inference workflows, storing models in HAQM S3 and implementing API-based real-time batch inference applications. 

By adopting HAQM SageMaker, NSUS transformed its labor-intensive, rule-based system into an automated ML-powered solution. This advanced system analyzes large datasets of user behavior in real-time, enabling proactive risk assessment and fraud prevention while significantly improving operational efficiency.

Lim says, “HAQM SageMaker allows NSUS to evaluate money laundering risks more effectively, building a stronger foundation for fraud prevention and fostering a healthier gaming environment.” The transition streamlined NSUS’s anti-money laundering operations and established a sophisticated fraud prevention system, ensuring a secure gaming experience for its global user base and demonstrating its commitment to innovation and regulatory compliance in the online poker industry.