The document discusses the advantages of Terracotta's BigMemory in addressing big data challenges in financial services, focusing on real-time data processing for fraud detection, transaction processing, and risk management. It highlights how BigMemory enhances application speed, scalability, and reliability, enabling firms to meet stringent performance SLAs. Several case studies illustrate substantial improvements in operational efficiency and profitability achieved by companies using BigMemory.

1. FEATURED SPEAKERS
The BigMemory Geoff Lunsford
Sales Director, Americas
Terracotta
Revolution in Karthik Lalithraj
Financial Director, Global Technical Services (East)
Terracotta
Services TERRACOTTA WEBCAST SERIES

2. Your speakers for this webcast
Photo Photo
Geoff Lunsford Karthik Lalithraj
Sales Director, Americas Director, Global Technical Services (East)
Terracotta Terracotta

3. Financial services companies have a variety of
Big Data challenges
 Big Data is not just analytics!
 You have a Big Data problem if you want to
speed up your applications in any of these areas:
– Trade and transaction processing
– Risk mitigation & fraud detection
– Customer service & support
– Portfolio valuation
– Compliance-mandated reporting
 But fast access to large volumes of data means
better decisions and increased profitability
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4. The in-memory revolution: From disks and
milliseconds to RAM and microseconds
90% of Data in 90% of Data in
Database Memory
Memory MODERNIZE
Database
Using an in-memory store with
App Response Time DB-like capabilities:
 High Availability
Milliseconds
 Persistence
 Data Consistency / Coherency
 Transactions
 Query
 …
App Response Time
Microseconds
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5. Plummeting RAM prices and exploding volumes of
valuable data make real-time Big Data possible
In-Memory Big Data
Maximize inexpensive memory Unlock the value in your data
Explosion in
Steep drop in
volume of
price of RAM
business data
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6. Terracotta BigMemory powers real-time
Big Data applications across many industries
 Fraud detection slashed from Terracotta customers
45 minutes to mere seconds
 Media streaming in real time
to millions of devices
 Customer service transaction
throughput increased by 100x
 Flight reservations load on
mainframes reduced 80%
 Highway traffic updates
delivered to millions of global
customers in real time

7. That’s because in-memory computing solves
big challenges facing CIOs
Scale and Decoupling from
Real-time Mainframe
performance databases
Big Data modernization
in the cloud for agility
in-memory data store

8. Financial services firms have been especially
quick to adopt Terracotta BigMemory
 30% of Fortune 500 banks use
BigMemory
 World’s largest credit card and
online transactions processors use
BigMemory
 Most popular financial services use
cases:
– Real-time fraud detection at Big Data scale
– Real-time portfolio valuation at Big Data scale
– Real-time transaction/payment processing at
Big Data scale
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9. BigMemory lets you use all the RAM available
in your servers, without expensive tuning
Without With
BigMemory BigMemory
Applications can Applications can
store only a few use ALL
GB of data in available RAM
RAM before while achieving
garbage extremely
collection low, predictable
degrades latency at any
performance. scale.
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10. BigMemory Max is the hub of a new
in-memory architecture for financial services
In-memory Speed
Get low, predictable latency
(microseconds at TB scale)
Simple, Fast to Deploy
Use Java’s defacto Ehcache API
Scale up
Massive Scale
Keep as much data in memory as
your data center can hold
Scale out
Data Consistency Guarantees
Ensure data stays in synch across
the array
Fault-tolerance + Fast Restart
Get 99.999% availability thanks to
mirrors and persistent backup
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11. CUSTOMER SUCCESS STORIES
Terracotta BigMemory in
Financial Services
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12. Fortune 500 online payments processor
Boosting profits through real-time fraud detection
 What the company was after
– Tens of millions of dollars in additional profit by improving fraud detection speed and
accuracy (30 cents of every $100 lost to fraud)
 Before BigMemory
– Adding one new rule to fraud detection algorithm would save $12 million
annually, but performance at scale only allowed 50 rules
– Company failed to meet 800 millisecond SLA for fraud detection
– Impossible to meet SLA with existing architecture
 After BigMemory
– Reduced fraud processing time to less than 500ms
– Thousands of rules added to fraud detection algorithm
– 99.999% completed transactions
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13. Fortune 100 commercial bank
Meeting SLAs for end-of-day trade reconciliation
 What they were after
– CIO had to meet 4-hour SLA for end-of-day reconciliations
 Before BigMemory
– Unable to process trade reconciliations within 4-hour window
– 240GB of trades, asset prices, etc. kept in slow, disk-bound databases
– End-of-day reconciliation was infamous as the firm’s most unstable and underperforming
application
 After BigMemory
– Consistently meeting 4-hour SLA by improving speed by 3x
– Terracotta BigMemory processing 500GBs of trade reconciliations
– Application went from the firm’s most unstable and underperforming to its most stable
and best performing in 3 months
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14. Fortune 20 commercial bank
Delivering collateral automation to 1000s of global clients
 What they were after
– With demand rising for collateral automation (real-time re-pricing, re-allocation), the
business wanted to build a new “virtual global longbox” for real-time views of collateral
positions anywhere in the world
 Before BigMemory
– Disk-bound database bottlenecks made scaling impossible
– Difficult to pull data from many sources for pricing, allocation and asset recall
– Not possible to scale as needed with existing infrastructure
 After BigMemory
– Terracotta Big Memory solution provides real-time access to assets, securities, collateral
across multiple accounts.
– BigMemory keeps 200GB of prices and portfolio data in memory for ultra-fast re-pricing
and allocations
– BigMemory and Quartz allowed the firm to increase volume of collateralized loans and
more effectively complete with competition
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15. BigMemory + Hadoop: Real-time Intelligence
Request
Real-time response
(e.g., "Is this
"Yes" or "No,"
transaction
informed by latest
Fortune 500 fraudulent?"
intelligence
online payments REAL-TIME INTELLIGENCE
REAL-TIME INTELLIGENCE
processor
BigMemory
Working
together, BigMemo
ry and Hadoop are
Hadoop feeds
Hadoop feeds BigMemory feeds
BigMemory feeds
creating a virtuous Long-term,
BigMemory with
BigMemory with Real-time latest
Hadoop with
Hadoopwith latest
cycle for real-time intelligence data
iterative about
intelligence about transactions to
transactions to
in-memory data
intelligence around analysis
fraud patterns
fraud patterns improve intelligence
improve intelligence
fraud detection.
DEEP (SLOW) INTELLIGENCE
DEEP (SLOW) INTELLIGENCE
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16. What could you do with instant
access to all of your data?
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17. Q&A
QUESTIONS?
Type them in the “Question” panel or in the chat window
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18. GET BIGMEMORY
1. Learn more + get your free download:
terracotta.org/bigmemory
2. Contact us:
geoff@terracottatech.com, sales@terracotta.or
g
3. Follow us on Twitter: @big_memory
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