XAP FOR FINANCIAL SERVICES

AGENDA

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The Business Case for Messaging Standards

Common Message Standards in the Financial Vertical

Technical Challenges

GigaSpaces XAP

What’s Next

THE DRIVE FOR MESSAGING STANDARDIZATION

Speed Up Trade Processing Reduce risk Support ever-growing

transaction rates

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THE DRIVE FOR MESSAGING STANDARDIZATION

Reduce Fragility and Trade Processing Costs Decrease manual intervention Standardize IT system achieving uniform protocols shared

by banks and different markets Transform back office silos and diversity into uniform stack

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MESSAGING STANDARDS IN THE FINANCIAL MARKETS

FpML (Financial products Markup Language )

FIXML (Financial

Information eXchange Markup

Language)

XBRL (eXtensible

Business Reporting Language)

Swift / ISO 20022

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SWIFT UTILIZATION OF STANDARDS

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Pre-Trade / TradePost- Trade / Pre-

settlement

Clearing and settlement reporting

Asset servicingPortfolio

administration

Payments and cash

management

Collateral Management

Financing securities

lending and borrowing

IOI/ Quotes, Trade, Execution, Pre-allocation

Trade allocation, Trade affirmation, confirmation, notification and matching, Transaction reporting

Settlement instructions, confirmation, Statements of pending settlements, Statements of movements

Data distribution, Corporate actions, Proxy voting

Total portfolio administration

Payments initiation, Cash reporting, Exception and investigation

Margin calles, Reponse, Administration

Repo, Reverse repo, Trade confirmation and matching, Settlement and reporting

ISO, FIX ISO, FIX, FpML ISO ISO ISO ISO ISO ISO

Equities, Fixed Income, Listed derivatives

Equities, Fixed income, Listed derivatives FX, MM and FX options, Syndicated loans, Commodities, OTC derivatives

Equities, Fixed income, Listed derivatives

Equities, Fixed income, Listed derivatives

Cash, Funds Cash Cash, Securities and bank guaranties

Equities, Fixed income, Repos

WHAT IS NEEDED

A fast, scalable and reliable data integration platform to bridge front office and back office Allow Straight Through Processing for many

millions of trades Better reconciliation through complex real time

matching

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THE TECHNICAL CHALLENGES

Support for complex yet fast queries over large set of data: Deeply nested object graphs Many millions (or even billions) of objects

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THE TECHNICAL CHALLENGES

Store massive amounts of data for the longer term: Tens of millions of messages a day, or more… Working set is relatively small (typically up to a

few days worth of data) But longer term analysis (and regulation) requires

data to be persisted for years

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THE TECHNICAL CHALLENGES

Quickly adjust to ongoing standards updates and message format variations Reduce time to market Reduce unexpected runtime errors and mistakes

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THE TECHNICAL CHALLENGES

Easily implement various event based workflows using the same data platform By processing the data in place Or as it flows into the system

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THE TECHNICAL CHALLENGES

Provide a native, easy to use programming interface for developers I work in Java / Scala (or

any other language for that matter)

I want my type safety I want my intellisense

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STEP BACK – IN MEMORY COMPUTING

“In memory computing (IMC) … provides transformational opportunities. The execution of

certain-types of hours-long batch processes can be squeezed into minutes or even seconds …

Millions of events can be scanned in a matter of a few tens of millisecond to detect correlations and patterns

pointing at emerging opportunities and threats "as things happen.”

IN MEMORY IS MORE ECONOMICAL THAN BEFORE

THE PROBLEM

Current architectures

Perform complex calculations in real time to improve your business performance, e.g. recommendations/promotions in an e-commerce web site, instant risk analysis / reconciliation in investment banks (STP)

IMC FOUNDATIONS

Ultra High Performance

High Availability

Linear Scalability

PUTTING IT INTO YOUR STACK

GigaSpaces

KEY FEATURES

Schema Free Data Model schema-free data API that supports upgrading the application’s data model on the fly

IndexingPredefined and ad-hoc property indexing for blazing- fast data access

QueryingSophisticated query engine with support for SQL and example queries

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Data partitioning Transparent content-based data partitioning to evenly and intelligently distribute data across servers

Fully ACID TransactionsLocal, distributed or XA

Two Way NoSQL IntegrationFor long term data storage and loading

CORE CAPABILITIES - DATA

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Native to Scala and Java/Spring Use Scala-native constructs such as immutable objects, predicates and closures

Strong Eventing Support React in real time, in place, to changes to the data

In-Grid, Distributed Code Execution Dynamic code loading and map/reduce like execution across the grid for optimized processing and data access

CORE CAPABILITIES - DATA

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® Copyright 2011 Gigaspaces Ltd. All Rights Reserved21

Challenge Solution

Frequent message format changes Schema free model

Complex, sub second queries XPath like queries on objects and strong indexing support

Store massive amounts of data for the longer term

Built in NoSQL integration

Process messages as they flow into the system

Event containers and distributed code execution

Developer friendliness Native Java/Spring, Scala support

MEETING THE CHALLENGES, OR WHY USE XAP

Thank You!

® Copyright 2011 Gigaspaces Ltd. All Rights Reserved 22