Investment Banking Technology March 17, 2011 Nic Arnold Global Head of Credit IT, UBS Investment Bank PUBLIC Warwick Computer Science

Investment Banking Technology

March 17, 2011

Nic ArnoldGlobal Head of Credit IT, UBS Investment Bank

PUBLIC

Warwick Computer Science

www.ubs.com/graduates

Table of Contents

SECTION 1 Finance 101 - Key Concepts in Credit TradingSECTION 2 Grid ComputingSECTION 3 Real time Business IntelligenceSECTION 4 How do we manage IT?SECTION 5 Joining UBS

SECTION 6 Appendix: The Credit Crunch


About me

Nic Arnold

Global Head of Credit IT, UBS Investment Bank

Joined UBS November 2009

Masters graduate in Electronic Engineering

12 years in Investment Banking IT at JP Morgan, Bank of America, Credit Agricole


UBS—metrics to measure a firm

Market capitalization

Tier 1 capital ratio

Long-term credit ratings

Credit default swap rate—5yr

Company Moody’s/S&P/Fitch

Credit Suisse Aa2/A+/AA- Deutsche Bank Aa3/A+/AA- JPMorgan Aa3/A+/AA- UBS Aa3/A+/A+ Goldman Sachs A1/A/A+ BofA A2/A/A+ Citigroup A3/A/A+ Morgan Stanley A2/A/A

Company CDS

JPMorgan 72 Credit Suisse 89 UBS 88 Deutsche Bank 91 Bank of America 160 Goldman Sachs 118 Citigroup 140 Morgan Stanley 159

Notes: Market capitalization, Credit ratings and Tier 1 capital ratio as of the quarter ended 31 December 2010 Credit Default Swap Rate as of 31 January 2011

1 No.2 FX House (Euromoney 2005-2010); Best Bank and Financial Sector (FinanceAsia, Fixed Income Poll 2009), No.1 European Equity Research Team (Institutional Investor 2002-2010)2,3 As of the quarter ended 31 December 20104 Best Private Banking Services Overall (Euromoney 2004-2009)

Company Mkt. cap. (US$bn)

JP Morgan 166 Citigroup 142 Goldman Sachs 86 UBS 62 Deutsche Bank 59 Credit Suisse 55 Morgan Stanley 46

Source: Bloomberg

Source: Companies’ reporting

Source: Companies’ reportingSource: UBS

Rated as a top investment bank in industry surveys1

Approximately USD62bn market capitalization2

Some CHF2.18tr in assets under management3

An award-winning wealth manager4

Nearly 150 years experiencein wealth management

One of the world’s largest asset managers

Company Tier 1 ratio (%)

UBS 17.7 Credit Suisse 17.2 Morgan Stanley 16.4 Goldman Sachs 16.0 JPMorgan 12.1 Citigroup 12.9 BofA 11.2


UBS—our services

Wealth Management Americas

Global Asset Management

Wealth Management & Swiss Bank

Wealth Management & Swiss Bank focuses on delivering comprehensive financial services to high net worth and ultra high net worth individuals around the world – except those served by Wealth Management Americas – as well as private and corporate clients in Switzerland.

Our Wealth Management business unit provides clients in over 40 countries, including Switzerland, with financial advice, products and tools to fit their individual needs. Our retail & Corporate business unit has a leading position cross its client segments in Switzerland.

Global Asset Management is a large-scale asset manager with businesses diversified across regions, capabilities and distribution channels.

It offers investment capabilities and investment styles across all major traditional and alternative asset classes. These include equities, fixed income, currency, hedge fund, real estate and infrastructure investment capabilities that can also be combined in multi-asset strategies. The fund services unit provides legal fund set-up and accounting and reporting for all retail and institutional funds.

Wealth Management Americas provides advice-based relationships through financial advisors who deliver a fully integrated set of products and services specifically designed to address the needs of ultra high net worth, net worth and core affluent individuals and families.

It includes the Wealth Management US Business, as well as the domestic Canadian business and the international business booked in the United State

Investment Bank

The Investment Bank provides securities and other financial products and research in equities, fixed income, rates, foreign exchange and precious metals.

It also provides advisory services and access to the world's capital markets for corporate and institutional clients, governments, financial intermediaries and alternative asset managers and private investors.

Note:

1 As of the quarter ended 31 December 2010

Headquartered in Zurich and Basel, Switzerland, UBS has offices in more than 50 countries, including all major financial centers, and employs approximately 65,000 people. Under Swiss company law, UBS is organized as an Aktiengesellschaft, a corporation that has issued shares of common stock to investors. UBS AG is the parent company of the UBS Group of companies (the “UBS Group”). The operational structure of the Group comprises the Corporate Center and four business divisions: Wealth Management & Swiss Bank, Wealth Management Americas, Global Asset Management and the Investment Bank.

UBS has CHF46,760 million¹ equity attributable to UBS shareholders, long-term credit ratings of Aa3/A+/A+ (Moody’s, S&P, Fitch) respectively), and invested assets of CHF2,152 billion¹.


UBS Investment BankThree main functions

Equities – distributing, trading, financing and clearing shares and share-linked products

Investment Banking Department – advising companies on their structure, funding, flotation, mergers & acquisitions

Fixed Income, Currencies & Commodities trading in Foreign Exchange, Interest Rates, Emerging Markets and Credit


SECTION 1

Finance 101 - Key Concepts in Credit Trading


101: How do banks operate?

The value of £100 today is £100, but in one year it may be more or less

It could go up – (earn interest) it could go down (be affected by inflation)

The Time value of money

But:

I’d really like the £100 back now

I may not be paid back - there is a risk attached to this loan

I could loan it to someone now for a year at 5 per cent

Discounted Cash Flow

How much this a £105 loan paid off in one year worth to me now? Present value

How much will my £100 be worth in one year? Future value

DCF techniques are used to estimate the money we receive and adjust for the time value of the money


101: How do businesses finance themselves?

A share of the ownership of the company. Each share has a value

Share price per = (Future Earnings + Assets - Liabilities) / Number of Shares

Issuing Equity

(Shares)

Taking a Loan

Loans pay interest – they have a cash flow

A bond is a debt security that entitles the holder to repayment of the principal sum plus interest.

Bonds have a face value (on which the issuer pays interest) a coupon (interest rate paid by issuer) and maturity (date that the principal is due to be paid)

Issuing Debt

(Bonds)

Bonds pay interest – they have a cash flow


101: Derivatives, Desks and Books

E.g. an Option - a contract giving the right to buy (call option) or sell (put option) a quantity of something at a set price (strike price) within a certain period of time (prior to the expiration date). Buyer pays a fee

Derivatives

Trading Book

The portfolio of financial instruments held by a brokerage or bank.

Each book can contain 1000’s of trades

Are occupied by licensed traders, usually specializing in trading one particular type of investment product

(e.g. foreign exchange traders, commodities traders, equities traders)

Trading desks

A security whose price is dependent upon or derived from one or more underlying assets.

The derivative itself is merely a contract between two or more parties. Its value is determined by fluctuations in the underlying asset


101: How do banks operate? Managing the Risk of trading

Market Risk – the risk of loss due to fluctuations in the market

The potential for the bank to experience losses due to fluctuations in prices of securities

Every day your portfolio value will change depending on the market price of each trade within it

Credit Risk is the risk of loss arising from a client who does not make payments as promised.

Such an event is called a default

Particularly in transactions lasting several years (e.g. a loan or mortgage) you may need to make financial provisions against the risk of default.

Understanding Credit Risk is an essential component of managing the bank’s balance sheet

Credit Risk – the risk of loss due to non payment


101: Securitized products: What is a CDO?

A Collateralized Debt Obligation is a securitization in which a purchased portfolio of any security is transferred into a Special Purpose Vehicle (e.g. a trust). The SPV then issues debt securities of varying seniority and equity to fund the purchase of the portfolio.

AAA-rated Notes

AA-rated Notes

A-rated Notes

BBB-rated Notes

BB-rated Notes

CDO equity

Credit exposure

DiversifiedPortfolio of Cash flow generating Credits

Last loss

5th Loss

4th Loss

3rd Loss

2nd Loss

1st Loss

Assets Liabilities Losses


101: CDO Pricing is a little bit complicated

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101: Calculating risk and understanding our trading position

Risk Management Market Risk (VaR, Stress) – probable loss on portfolio given changes in the market

Credit Risk (PFE, CVA) – probable loss on portfolio given default of counterparty

Profit and Loss explain (PnL) – daily change to the value of our trading positions

Sensitivities – of prices due to potential changes in underling parameters on which their value is dependent

Collateral – how can we net this/optimize our use of collateral?

Front Office (The Traders)

Position keeping, sensitivities

FX book management

Real time P&L – present value of our trading positions

Continuous hedging – protect trading positions against potential shifts in the market


101: Monte Carlo Simulations

Monte Carlo is a powerful simulation method for estimating the future performance of a portfolio.

Relies on repeated random sampling to compute results

Simulate sources of uncertainty affecting value, and then determine average value over the range of resultant outcome

Monte Carlo simulation allows us to evaluate a wide range of market conditions and investment scenarios

Time

Monte Carlo SimulationValue

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For illustrative purposes only.

Monte Carlo Simulation


101: Monte Carlo simulations – Computing challenges

Monte Carlo simulations are great because…

Monte Carlo simulations are generic – you can model any cash flow

They provide a Probability Distribution

But… they are compute intensive…

You can optimize your model

You can pre-generate random numbers as input

You can optimize your software to enable them to run faster

You can run them on the Grid

Emerging requirement to aggregate multiple Monte Carlo simulations together to incorporate multiple types or sources of risk


101: The price and the risk of every trade in a portfolio

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Several hundred scenarios per trade

Multiple types of trade

Thousands of trades

Future

Option Swap

Forward

AAA-rated NotesAA-rated NotesA-rated NotesBBB-rated NotesBB-rated NotesCDO equity










Delivered quicklyLots of data items


101: Supporting global 24 hour trading

There is little opportunity to shut a system down to fix it.

Time is Money

If trading closes at 5pm in New York on Friday, there are only 36 hours before Sydney opens, in which we can fix or replace systemsUBS FX/MM Trading


From Sub Prime to Credit Crunch

Mortgages and loans produce a cash flow

Sub prime: Making loans to people who may have difficulty repaying

The Subprime mortgage crisis arose from 'bundling' American subprime and American regular mortgages into Asset-backed securities (ABS/MBS) and selling these bundles onward

1. Real Estate prices peaked in the US in 2006

2. Leading to a rise in subprime mortgage defaults during 2007

3. Leading to an increase in losses reported within ABS/MBS

4. During 2008 finance institutions wrote off $750 billion in associated losses

5. ….which led to Banks experiencing reduced access to funds, plunging share prices.


CDX Credit Default Swap Index 2007 to Feb 2011

HBOSRBS

Lehmans Merrill

Bear StearnsNorthern Rock

SubprimeCrisis

US eases money

Greece in Trouble

Ireland in trouble

Citigroup

Icelandin trouble

US Stimulus Package

AIGWAMU

Spain onwatch

AIG loss

GM&Chrysler bankrupt


Financial markets moved from Subprime to Recession

Immediate consequences and measures

Financial institutions affected most strongly

Phase 3 Economic recession

Risks for non-performing loans/other assets con-tinue to threaten small or tiny capitalized banks

Retail banks with large exposure to consumer financing, corporate loans, and European real estate

The whole economy going into recession or lower-growth periods

Phase 2 Liquidity crunch

M&A, bankruptcy/liquidation of troubled institutions

State-driven recapitalization and nationalization efforts

Banks with significant imbalance in nature and maturity of their assets & liabilities and strong exposure to wholesale debt funding

Crisis spreading quickly, liquidity in interbank market drying up

Phase 1 Subprime crunch

Large writedowns and need for capital increases

Governments rescue plans to restore market for distressed assets

Banks holding positions in the affected markets

Markets for sub-prime and related derivatives are collapsing

Lifecycle stage of crisis


SECTION 2

Grid Computing


Sequential Computing

Single Threaded Excel typically calculates cells sequentially based on dependencies

Multi Threaded Excel can calculate in parallel but is limited to the number of processors on machine

The 55 cells (or 55 units of work) in the example would take 10 seconds each to calculate

A single cpu/core machine would take nearly 6 minutes to calculate this simple example

Do we continue buying more computers?


Parallel Computing on the Grid

Compute Server 1 Compute Server 100Compute Server 2 Compute Server 3 Compute Server 4 . . .

Grid ControllerA1 B1 C1 D1 E1A2A3A4A5A6A7A8A9

A10

B2 C2 D2 E2B3 C3 D3 E3B4 C4 D4 E4B5 C5 D5 E5B6 C6 D6 E6B7 C7 D7 E7B8 C8 D8 E8B9 C9 D9 E9

B10 D10 D10 E10

A1 C4 E5 D9 A8

Excel Add-in communicates with the Grid.

Grid Software divides up with the compute into single work units, so 55 separate units of work, bundling the task inputs. 5 second overhead in setting this up.

The 55 units of work are then allocated to Grid engines to run the compute.

As the compute engines complete their calculations they notify the Grid Controller of the result.

Controller aggregates the results and sends back to Excel. Further 5 second overhead. Excel presents the data as appropriate.

6 min calculation reduced to ~ 20 secs

Distributed, parallel computation – break long running calculations into many pieces and run in parallel across many computers


Grid Computing

24,000 Dedicated

Cores


Evolution of the Grid

0

10000

20000

30000

40000

50000

60000

70000

2003 2004 2005 2006 2007 2008 2009 2010 2011

Manager and Brokers control access to Grid

Desktop communicates with the Grid

Apps with similar profiles share resources on Grid

Tasks submitted to a single manager

The compute engine is passed a reference to the inputs on the client

The engine transfers the inputs directly

Compute starts

When complete the client is notified and pulls the result directly from the compute engine.

In busy times middleware queues up tasks

Workload is submitted by a command line engine – allows the submission of work on the grid to be automated. (Excel gone)

Multiple Brokers controlled by a central Manager, which directs resources based on workload.

Desktops are scavenged

All tasks can run on all engines

Applications with complementary profiles share resources

Offsite compute engines used to complement internal capacity

Workloads are complicated, so pools of resources are matched to workload across 30 brokers

Grid Engine GrowthStarting Count


2010: GridServer introduces resource tiering

DataSynapse GridServer allows use policies to be applied to the grid

Compute Servers are grouped together based on physical location and homed to Resource brokers.

Multiple Worker Brokers run compute jobs, separated by Application and Criticality (i.e. Trader Critical, Batch, MultiDay SLA and Test)

Compute servers will first complete work on the top tier, when there is no work pending they move to the second tier (multiple brokers can exist on each tier), etc

TIER 1TRADER CRITICAL JOBS

TIER 2OTHER OVERNIGHT JOBS, SUCH AS MARKET RISK, P&L AND OTHER DOWNSTREAM FEEDS ACROSS THE GLOBE

TIER 3MULTIPLE DAY SLA JOBS (VERY BIG JOBS THAT CAN’T BE RUN DAILY) , TEST & UAT JOBS


Grids segmented

by workload type &

volatility

Brokers assigned per

workload / Stream

Engines assigned to

home broker for

primary workloads

Engine sharing less dynamic, limited to “like” workloads within

same grid

Additional engines required

Shared engine

Red Lines indicate the possibility for an emergency manual capacity movement from non production to production for a

given serious production incident.

This should take between 15 minutes and 2 hours for the

capacity to be available on new grid

Our Next Generation Grid


How do we measure the effectiveness of our IT investment?

Reduce Hardware and Environmental cost - Application A uses compute resources during the day and Application B uses compute resources overnight

Grid Hardware doesn’t need to be reliable

Data Centres don’t need to be reliable

Developers more productive Utility / Cloud Computing – make use of these vendors

Reliability - Improve resilience by spreading the load transparently - Grid nodes can fail without taking down the whole Grid.

Scale up without a re-write

Add capacity quickly for Testing or in response to Market Events

Competitive Advantage Easy to support – just swap!


Current & future challenges for the Grid – feeding it!

Coherent data

Networks are not increasing in performance at the same rate as CPUs

Huge volume of data

Sequential or parallel calculations?

Sudden need for capacity – e.g. the ‘Flash Crash’ of May 2010?

CPU Scavenging?


UBS Investment Bank IT: Key Facts & Figures

>24,000 Grid Engines (dedicated cores)

>12,000 servers – grows 5% per year, half of all UBS’s servers used by the Investment Bank

>22,000 workstations

>1,800 applications in production

>2.4 Petabytes of storage – grows 12% per year

>31,000 system changes in 2009

+10 % Y/Y

>1.7 MW of power consumed = 1700 Kettles!

October 2010


SECTION 3

Real time Business Intelligence


The challenge of Timeliness AND Complexity

Decision making happens under pressure

Data is changing dynamically

Large amounts of data have to be analyzed/correlated

You need to visualize data in a way that supports decision making

You need to do this quickly


What is OLAP? (online analytical processing)

OLAP gives the capability of manipulating and analyzing data using additional dimensions e.g. by product, by city, by budget, by client

OLAP Cubes provide an extension to the two-dimensional (x&y) array of a spreadsheet

The OLAP cube’s metadata structure is created from a star schema or snowflake schema joiningseveral tables in a relational database

OLAP tools are sold by database and analytic software makers and are widely used in business to analyse historical data, e.g. for root cause analysis.

In the past, OLAP has been too slow in collecting and analyzing data to enable a business to make decisions on ‘live’ data streams e.g. market prices


Complex Event Processing (CEP)

Decision making based on analyzing streams of information in real-time

Uses a ‘SQL-like’ language to query an Event Stream

Event Streams can be live market prices, exchange trades

Detect patterns among multiple streams of event data

Execute calculations on multiple streams of event data – e.g. calculate averages

Information complexity is low but decision timeliness is fast

The combination of CEP and OLAP provides a Real-Time Business Intelligence platform that enables us to analyse the latest information and react immediately


What can we use Real-Time Business Intelligence for?

Sensing and responding to multiple live events with minimal latency between the event and decision point

Analyzing the impact of market changes on our portfolio in real time (by executing on-line algorithms in response to event data entering the system)

Detecting changes in prices to trigger trading decisions (Algorithmic Trading)

Communicating with management and collaborate on trading decisions in a credit sensitive environment.

Predicting Future Outcomes

….this could potentially lead to a decrease in risk and an increase in revenue..


A Real-time BI implementation…

In-Memory, Object-Based OLAP Server• Intuitive data representation, multiple systems consolidation

• Object-oriented (vectors, matrices, …)

• Non-linear aggregations (netting, VaR,…)

• Very fast multi-threaded loading and aggregation (64B Java 1.6)

• Open choice of user interfaces:

Excel Pivot Table, Web browser, etc… (MDX / XMLA)

C#, .NET, Java, etc… (Web Service)

Real-Time (Incremental) Transactional Engine - CEP• Multiple object input flow: Trade flow, Market data flow, Risk numbers etc.

• Push & Pull technology

• Alerts (continuous queries are executed against Cube)

An In-Memory system scanning incoming data streams to look for patterns to analyze the impact of market changes on our portfolio in real time…


High Level Architecture

Active Pivot(Real Time + EOD

Risk and PnLAggregation)

Trade(Laredo, CB10,

Julius,Stealth, OBS, CVA)

Market Data(FMM, OBS, FX)

Calculated Measures(fRisk, CB10, Julius,

Stealth, Panther)

Reference Data(CDR, BHS)

Warehouse for historical data

Acuity Boundary

Front Office UI(Aggregated Risk and PnL

across Credit business)

External Source Systems

(Authenticate)

(Authorize)Security Snapshot

Transform

Acuity Boundary

SPG UI(T0 Flash/T+1 Recon

Reports)

Structured Credit OLAPAnd

Omni Risk replacement

(T0 Flash) (T+1 FinanceEOD)

Finance UI(CDS fRisk/CDS CB10

Recon Reports)

(Struct CDS from fRisk)

(Struct CDSfrom CB10)

Manual Reconreplacement

Manual Reconreplacement

Our Trading Books

Live Market Prices

Our Risk Numbers

Reference Data

OLAP Data Store

Reports from Acuity are sent to the outside world.

User Interfaces

CEP detection and correlation


Performance Metrics

A Cube has

1 trillion data points

20 dimensions, 39 levels

5-days history held in memory

5-days – 250 GB loaded into cube (50 GB x 5 days)

1-year history available on disk

1 year – 13 Tb data is compressed into 1.5 Tb and loaded into cubes

A new day’s data must be accessible within 10 minutes

This translates into

Processing concurrent Updates: 200,000 updates/sec – one every 5 m/sec

Processing concurrent Queries: 266 queries/sec


Future Business Intelligence challenges

Real-time pre-trade credit checking across all product types

Real-time multi-step Monte Carlo risk simulations before every trade

Appropriate charging for clients and trading activities within the bank

Real-time business intelligence on everything!

More Stress tests (FSA, FINMA, SEC) to measure banks capital requirements

Scenario-based exercises – a set of risks or activities often employing multiple risk measures

Given the default of these counterparties/failure how much capital do you need to cover potential losses?

The ability to simulate stress


SECTION 4

How do we manage IT?


The challenge

Capacity

Functionality

Reliability

Text

Agility ‘Delivering innovation faster’

Does it behave as expected?

‘New capabilities to support a changing business’

‘Can you do it often enough?


UBS has 65,000 staff, 17,000 in IT, (5500 IT staff in the Investment Bank)

UBS IT: Our Geographic Footprint

Brazil

Hong Kong

Japan

Australia

RussiaAmericas

3660

UK2829

.

APAC4341

Switzerland

5601

EMEA801


How do we manage multi-site/offshore development?


Round the clock development in Singapore, Kiev, London, Stamford & Chicago

Communication, Training, Documentation even more important

How do you Test offshore teams code – or do they QA your own teams code?

Using Vendors

Technology

Small components with integration challenge v monolithic applications

Methodology: Agile - Scrum - KanBan

People

Formal Spec OR Have Vendor’s staff integrated into multi-site model

Clear Responsibilities, Communications and Process – good communication is essential

Controlled code - Shared code base – daily builds & automated unit testing

QA teams sited with developers in all sites – Test Driven Development


Scrum & KanBan

Split work into small, concrete deliverables

Split time into short fixed length cycles

Define workflow stages: Todo, Dev, Test, Done

Daily stand-ups (scrums) – 10 mins

Retrospectives

Methodology tailored to the team

Scrum

Limit work per cycle / iteration

KanBan

Limit work per workflow stage


Scrum / KanBan Board @ UBS


Three areas of investment in technology

Maintain Infrastructure & Licenses – (buying systems

& software)

Take advantage of price/performance increases

Maintain supportability of systems (right versions)

Use upgrades and automation to reduce cost of KTLO

KTLO – Keep the Lights On (Operate our IT systems)

Change global systems

Support global systems

Strategic Investment (building/buying new,

retiring old)

Support future revenue & profitability growth

Do this for less than it cost last year

Own and manage the entire IT spend


UBS Investment Bank IT: Award-winning Technology

UBS Delta: Most Innovative Actuarial Software Life & Pension Risk 2010

UBS Delta: Best Technology Vendor for Risk ManagementCredit Awards 2009

Technology Innovation AwardCredit Magazine 2009, 2010

Best Proprietary TechnologyBuy-Side Technology 2007

No.1 for Algorithmic TradingThomson Reuters Extel 2010

No.1 First for Overall Post-Trade ServicesZ/Yen McLagan 2009

No.1 for Post Trade Performance in FXMcLagan/Z-Yen 2010

No.1 for Post Trade Services – North AmericaZ/Yen McLagan 2009

Best in AlgorithmsAsianInvestor 2010

Best FX Post-Trade ServiceProfit & Loss 2010

Best All Round Solutions for Prime Broking Asiamoney 2006-2009

Best All Round Solutions for Prime Broking Thomson Reuters Extel 2001-2010

Best Firm for Portfolio Trading Execution Thomson Reuters Extel 2008-2010

Best product innovation for Prime BrokingAsiamoney 2009

Best Post Trade Services Global FX Week 2009

No.1 Internal Crossing The Trade 2010

Best Fixed Income Research and StrategyTechnical Analyst Magazine 2009

No.1 for Currency Forwards, Swaps and Vanilla Options Risk 2009


We have received numerous awards for our inclusive culture

Top 10 Best Places to InternBusiness Week 2009

Top 100 Graduate Employers The Times High Fliers 2007-2009

Top 50 Best Workplaces in the UKFinancial Times

No.1 Employer of Choice for Business Graduates in Switzerland Bilanz/ Universum 2007-2008

Top 100 Best Workplaces in EuropeFinancial Times

Lord Mayor’s Dragon Award for Excellence in Corporate Responsibility and Community ActivitiesDragon Awards, 2004, 2008, 2010

Perfect score for Corporate Equality Index HRC Campaign Foundation 2009-2010

100 Best Companies for Working Mothers (US)Working Mother Magazine 2003-2008

Employer Choice for Women, AustraliaEOWA 2009

Top 20 Employers in Asia PacificThe Best Employers in Asia study, conducted by Hewitt Associates

Top 100 Employers for Lesbian, Gay and Bisexual People in BritainStonewall Workplace Equality Index

It’s a career not just a job

We are a truly global technology development organization

Technologists are highly valued – we make our own software

There’s a very real connection between the technology you create and our business performance


SECTION 5

Joining UBS


Graduate Training Program (GTP) – Your future @ UBS

24 months

Building the network

The UBS GTP program will equip you with the skills, knowledge and experience to achieve long-term personal and professional goals and challenge you to achieve your potential.

You'll learn the business while developing your technical skills to deliver real world solutions.

By the end of the two year program you’ll be an integral part of the UBS Group Technology team.

Into the future

Joining the teamGetting on board

Role Specific TrainingTechnical training based on GTP’s individual learning needs

GTP DiscoveryGTP intro to UBS & Financial Services

GTP FundamentalsGT Strategy, Business Model & Operating Principles

Rotation

Permanent Placement


Ready for take off?

Without our systems and infrastructure, we wouldn’t be in business …

Apply today at www.ubs.com / graduates

Start awards slide show

Documents

Investment Banking Technology March 17, 2011 Nic Arnold Global Head of Credit IT, UBS Investment Bank PUBLIC Warwick Computer Science