Software AG Case Studies Global Financial Group Earns Millions in Credit Card Profit

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	Global Financial Group Earns Millions in Credit Card Profit Software AG

Global Financial Group Earns Millions in Credit Card Profit

Software AG

Technology Category	Analytics & Modeling - Real Time Analytics
Applicable Industries	Finance & Insurance
Applicable Functions	Business Operation
Use Cases	Fraud Detection
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Real Time Analytics

Applicable Industries

Finance & Insurance

Applicable Functions

Business Operation

Use Cases

Fraud Detection

Services

Data Science Services

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.

About 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.

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.

Impact #1	The company meets its SLA more than 99.995 percent of the time after implementing BigMemory Max.
Impact #2	The company’s significant store of in-memory blacklist data is protected by BigMemory’s persistent storage and fault-tolerant, fast-restartable architecture.
Impact #3	WAN replication keeps its global data centers in sync, making it easy to redirect traffic from one to another in case of outages or heavy loads.

Impact #1

The company meets its SLA more than 99.995 percent of the time after implementing BigMemory Max.

Impact #2

The company’s significant store of in-memory blacklist data is protected by BigMemory’s persistent storage and fault-tolerant, fast-restartable architecture.

Impact #3

WAN replication keeps its global data centers in sync, making it easy to redirect traffic from one to another in case of outages or heavy loads.

Benefit #1	Saved millions of dollars by flagging blacklisted cards faster
Benefit #2	Improved protection for 10GB of data

Benefit #1

Saved millions of dollars by flagging blacklisted cards faster

Benefit #2

Improved protection for 10GB of data

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Overview

Global Financial Group Earns Millions in Credit Card Profit

Operational Impact

Quantitative Benefit