Anti-Fraud Data Lake Implementation for Retail Chain

Objective: Support a leading convenience store chain with over 14,000 locations across Latin America in reducing financial losses caused by false positive fraud alerts. The goal was to improve transaction accuracy, protect revenue, and preserve critical partnerships with banking institutions that provide in-store financial services.

Key Goals:

• Reduce the rate of false positive fraud detections.
• Enhance fraud detection accuracy through advanced analytics.
• Integrate a high-performance anti-fraud engine into daily operations.
• Cleanse and consolidate transaction data from multiple systems.
• Protect existing financial partnerships and related revenue streams.

Challenges:

• High False Positive Rates: Existing fraud processes flagged valid transactions, leading to lost revenue and customer friction.
• Financial Risk Exposure: Inaccurate transaction monitoring risked termination of key banking partnerships.
• Data Fragmentation: Transaction data was dispersed across various systems, lacking standardization and integration.
• Lack of Automation: The absence of automated data transformation processes delayed fraud detection improvements.

Solutions:

• Data Lake Deployment: Implemented a centralized Big Data architecture to aggregate, cleanse, and transform transaction data.
• Advanced Fraud Analytics: Integrated SAS’s industry-leading anti-fraud solution to analyze patterns and flag high-risk activity with precision.
• End-to-End Process Automation: Automated the flow of structured, enriched data into the SAS platform to enable real-time insights and alerting.
• Scalable Architecture: Built a flexible solution architecture capable of evolving with new business rules and financial products.

Technology Used:

Big Data Lake Platform, SAS Anti-Fraud Solution, Data Cleansing & Transformation Pipelines, API Integration, Real-Time Analytics Engine

Results:

• Reduced false positive transactions by delivering $8MM in net annual savings.
• Increased annual sales by $20MM through accurate fraud filtering and smoother customer experiences.
• Protected up to $30MM in potential lost commissions by preserving key banking partnerships.
• Established a scalable fraud management framework to support future store growth and digital payment channels.

Conclusion: By combining Big Data architecture with best-in-class fraud detection, the client significantly reduced revenue leakage and protected vital financial partnerships. This solution not only strengthened the company’s fraud defenses but also positioned it to expand its financial services footprint with data-driven confidence.