Faster Fraud Detection Drives Higher Profits
Customer Company Size
Large Corporate
Country
- Worldwide
Product
- BigMemory Max
Tech Stack
- Oracle Exadata
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Fraud Detection
Services
- Data Science Services
About The Customer
The customer is a leading global online payments company that serves as a clearinghouse for online and credit card payments. The company operates on a global scale and is heavily reliant on its ability to effectively manage risk to maintain profitability. Fraudulent transactions were costing the company around 30 cents of every $100 in transactions. The company's risk management team identified that enhancing the company’s fraud detection algorithm with one additional rule would save $12 million annually. However, the company's existing platform could only handle 50 rules, and the team wanted to add thousands more to improve the accuracy of fraud detection.
The Challenge
The company, a global clearinghouse for online and credit card payments, was facing significant losses due to fraudulent transactions. The existing fraud detection system could only handle 50 rules, but the risk management team wanted to add thousands more to enhance the accuracy of fraud detection. Additionally, the company wanted to reduce transaction time to achieve real-time fraud detection in all cases. The existing platform had an end-to-end Service Level Agreement (SLA) of 800 milliseconds, which was not met 10% of the time, resulting in either approval of the transaction or payment of a fine, costing an estimated $10 million each year.
The Solution
The company decided to implement BigMemory Max to improve its fraud detection capabilities. The existing platform, Oracle Exadata, was used to store fraud detection rules in disparate disk-bound databases, but it was limited in throughput and had inconsistent latency. A proof-of-concept implementation demonstrated that BigMemory could keep 100x the number of rules in a central, TB-scale in-memory store and boost throughput by over 30x, to 65,000 transactions per second. After confirming that BigMemory delivered extremely low, predictable latency at scale, the company was confident that BigMemory was the ideal choice for adding algorithmic complexity and accuracy, and also for meeting the new, stricter SLA.
Operational Impact
Quantitative Benefit
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