Global Financial Group Earns Millions in Credit Card Profit
Customer Company Size
Large Corporate
Country
- United States
Product
- BigMemory Max
Tech Stack
- Java
- Oracle Database
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 Fortune 300 global financial group. The company's credit cards are accepted at millions of merchant locations and more than 845,000 ATMs in more than 185 countries. The company was facing a challenge with meeting its Service Level Agreements (SLAs) 99.995 percent of the time. The company was losing an estimated $10 million annually due to the improper acceptance of blacklisted cards. The company's list of seven million blacklisted card numbers and individuals was kept in a disk-bound Oracle® database, which was slow to access. The Java® garbage collector was causing extended, unpredictable pauses, forcing the company to “guess” on many transactions and exposing the company to losses.
The Challenge
A leading credit card company was failing to meet its one-second Service Level Agreement (SLA) for identifying blacklisted credit cards approximately 0.3 percent of the time. This resulted in blacklisted cards being improperly accepted, costing the company an estimated $10 million annually. The causes were twofold. First, the company kept its list of seven million blacklisted card numbers and individuals in a disk-bound Oracle® database, which was slow to access. Second, the Java® garbage collector caused extended, unpredictable pauses. Each pause forced the company to “guess” on many transactions, exposing the company to losses.
The Solution
The company wanted to move its entire list of blacklisted cards and individuals into machine memory to dramatically improve access speed. It considered Oracle® Coherence, but ultimately concluded that only Terracotta BigMemory could deliver advanced in-memory data management as well as extremely low and predictable latency at scale—all without garbage collection pauses or expensive and time-consuming Java tuning. In addition, Terracotta’s WAN connector performed flawlessly in replicating its in-memory data sets across global data centers. The company implemented BigMemory Max to improve the performance of applications that detect blacklisted cards.
Operational Impact
Quantitative Benefit
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