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Investment Banking Technology

Warwick Computer Science. PUBLIC. Investment Banking Technology. Nic Arnold. Global Head of Credit IT, UBS Investment Bank. March 17, 2011. Table of Contents. SECTION 1 Finance 101 - Key Concepts in Credit Trading SECTION 2 Grid Computing SECTION 3 Real time Business Intelligence

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Investment Banking Technology

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

  2. Table of Contents SECTION 1 Finance 101 - Key Concepts in Credit Trading SECTION 2 Grid Computing SECTION 3 Real time Business Intelligence SECTION 4 How do we manage IT? SECTION 5 Joining UBS SECTION 6 Appendix: The Credit Crunch

  3. 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

  4. UBS—metrics to measure a firm • Rated as a top investment bank in industry surveys1 • Approximately USD62bn market capitalization2Some 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 Market capitalization Long-term credit ratings Source: UBS Source: Companies’ reporting Tier 1 capital ratio Credit default swap rate—5yr Source: Companies’ reporting Source: Bloomberg 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 2010 4 Best Private Banking Services Overall (Euromoney 2004-2009) 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

  5. UBS—our services 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. Wealth Management & Swiss Bank Wealth Management Americas Global Asset Management 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. 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. • 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 • 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. Note: 1 As of the quarter ended 31 December 2010

  6. UBS Investment Bank Three main functions Investment Banking Department – advising companies on their structure, funding, flotation, mergers & acquisitions Equities – distributing, trading, financing and clearing shares and share-linked products Fixed Income, Currencies & Commodities trading in Foreign Exchange, Interest Rates, Emerging Markets and Credit

  7. SECTION 1 Finance 101 - Key Concepts in Credit Trading

  8. 101: How do banks operate? The value of £100 today is £100, but in one year it may be more or less The Time value of money It could go up – (earn interest) it could go down (be affected by inflation) I could loan it to someone now for a year at 5 per cent But: I’d really like the £100 back now I may not be paid back - there is a risk attached to this loan Discounted Cash Flow How much will my £100 be worth in one year? Future value How much this a £105 loan paid off in one year worth to me now? Present value DCF techniques are used to estimate the money we receive and adjust for the time value of the money

  9. 101: How do businesses finance themselves? A share of the ownership of the company. Each share has a value Issuing Equity (Shares) Share price per = (Future Earnings + Assets - Liabilities) / Number of Shares Issuing Debt (Bonds) 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) Bonds pay interest – they have a cash flow Loans pay interest – they have a cash flow Taking a Loan

  10. 101: Derivatives, Desks and Books Derivatives 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 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 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 Trading Book The portfolio of financial instruments held by a brokerage or bank. Each book can contain 1000’s of trades

  11. 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 – the risk of loss due to non payment 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

  12. 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. Assets Losses Liabilities AAA-rated Notes Diversified Portfolio of Cash flow generating Credits Last loss 5th Loss 4th Loss 3rd Loss 2nd Loss 1st Loss AA-rated Notes A-rated Notes Credit exposure BBB-rated Notes BB-rated Notes CDO equity

  13. 101: CDO Pricing is a little bit complicated If then goto Lorem ipsum, lorem ipsum dolor sit amet, Lorem, dummy text, loren ipsumLorem ipsum, lorem ipsum dolor sit amet, Lorem, dummy text, loren ipsumLorem ipsum, lorem ipsum dolor sit amet, Lorem, dummy text, loren ipsumLorem ipsum, lorem ipsum dolor sit amet, Lorem, dummy text, loren ipsumLorem ipsum, lorem ipsum dolor sit amet, Lorem, dummy text, loren ipsumLorem ipsum, lorem ipsum dolor sit amet, Lorem, dummy text, loren ipsumLorem ipsum, lorem ipsum dolor sit amet, Lorem, dummy text, loren ipsumLorem ipsum, lorem ipsum dolor sit amet, Lorem, dummy text, loren ipsumLorem ipsum, lorem ipsum dolor sit amet, Lorem, dummy text, loren ipsumLorem ipsum, lorem ipsum dolor sit amet, Lorem, dummy text, loren ipsumLorem ipsum, lorem ipsum dolor sit amet, Lorem, dummy text, loren ipsum

  14. 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

  15. 101: Monte Carlo Simulations Monte Carlo Simulation Monte Carlo Simulation Value • 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 Distributionof final values Time For illustrative purposes only.

  16. 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

  17. 101: The price and the risk of every trade in a portfolio AAA-rated Notes AAA-rated Notes AAA-rated Notes AAA-rated Notes AAA-rated Notes AAA-rated Notes AAA-rated Notes AAA-rated Notes AAA-rated Notes AAA-rated Notes AA-rated Notes AA-rated Notes AA-rated Notes AA-rated Notes AA-rated Notes AA-rated Notes AA-rated Notes AA-rated Notes AA-rated Notes AA-rated Notes A-rated Notes A-rated Notes A-rated Notes A-rated Notes A-rated Notes A-rated Notes A-rated Notes A-rated Notes A-rated Notes A-rated Notes BBB-rated Notes BBB-rated Notes BBB-rated Notes BBB-rated Notes BBB-rated Notes BBB-rated Notes BBB-rated Notes BBB-rated Notes BBB-rated Notes BBB-rated Notes BB-rated Notes BB-rated Notes BB-rated Notes BB-rated Notes BB-rated Notes BB-rated Notes BB-rated Notes BB-rated Notes BB-rated Notes BB-rated Notes CDO equity CDO equity CDO equity CDO equity CDO equity CDO equity CDO equity CDO equity CDO equity CDO equity Thousands of trades If then goto Lorem ipsum, lorem ipsum dolor 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scenarios per trade Future Delivered quickly Lots of data items

  18. 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 systems UBS FX/MM Trading

  19. 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 • Real Estate prices peaked in the US in 2006 • Leading to a rise in subprime mortgage defaults during 2007 • Leading to an increase in losses reported within ABS/MBS • During 2008 finance institutions wrote off $750 billion in associated losses • ….which led to Banks experiencing reduced access to funds, plunging share prices.

  20. CDX Credit Default Swap Index 2007 to Feb 2011 HBOSRBS Lehmans Merrill Icelandin trouble AIGWAMU Citigroup AIG loss Greece in Trouble Bear StearnsNorthern Rock Spain onwatch US Stimulus Package SubprimeCrisis GM&Chrysler bankrupt Ireland in trouble US eases money

  21. Financial markets moved from Subprime to Recession Phase 1 Subprime crunch Phase 2 Liquidity crunch Phase 3 Economic recession Markets for sub-prime and related derivatives are collapsing Crisis spreading quickly, liquidity in interbank market drying up The whole economy going into recession or lower-growth periods Banks holding positions in the affected markets Banks with significant imbalance in nature and maturity of their assets & liabilities and strong exposure to wholesale debt funding Retail banks with large exposure to consumer financing, corporate loans, and European real estate Large writedowns and need for capital increases Governments rescue plans to restore market for distressed assets M&A, bankruptcy/liquidation of troubled institutions State-driven recapitalization and nationalization efforts Risks for non-performing loans/other assets con-tinue to threaten small or tiny capitalized banks • Lifecycle stage of crisis • Financial institutions affected most strongly • Immediate consequences and measures

  22. SECTION 2 Grid Computing

  23. 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?

  24. Parallel Computing on the Grid Distributed, parallel computation – break long running calculations into many pieces and run in parallel across many computers • 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

  25. Grid Computing 24,000 Dedicated Cores

  26. Evolution of the Grid Desktop communicates with the Grid Apps with similar profiles share resources on Grid Manager and Brokers control access to 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 70000 60000 50000 40000 30000 20000 10000 0 2003 2004 2005 2006 2007 2008 2009 2010 2011 Starting Count Grid Engine Growth

  27. 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

  28. Our Next Generation Grid 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 Grids segmented by workload type & volatility Brokers assigned per workload / Stream Engines assigned to home broker for primary workloads Additional engines required Shared engine Engine sharing less dynamic, limited to “like” workloads within same grid

  29. How do we measure the effectiveness of our IT investment? • Competitive Advantage • Easy to support – just swap! • 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

  30. Current & future challenges for the Grid – feeding it! Networks are not increasing in performance at the same rate as CPUs Coherent data Huge volume of data Sequential or parallel calculations? Sudden need for capacity – e.g. the ‘Flash Crash’ of May 2010? CPU Scavenging?

  31. 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

  32. SECTION 3 Real time Business Intelligence

  33. 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

  34. What is OLAP? (online analytical processing) • OLAP gives the capability of manipulating and analyzing data using additional dimensionse.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 astar 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

  35. 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

  36. What can we use Real-Time Business Intelligence for? Predicting Future Outcomes 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. ….this could potentially lead to a decrease in risk and an increase in revenue..

  37. 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…

  38. High Level Architecture Our Risk Numbers Live Market Prices Reference Data Our Trading Books CEP detection and correlation OLAP Data Store Reports from Acuity are sent to the outside world. User Interfaces

  39. Performance Metrics A Cube has • 1 trillion data points • 20 dimensions, 39 levels • 5-dayshistory held in memory • 5-days – 250 GB loaded into cube (50 GB x 5 days) • 1-yearhistory available on disk • 1 year – 13 Tb data is compressed into 1.5 Tband 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

  40. Future Business Intelligence challenges Real-time business intelligence on everything! 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 The ability to simulate stress 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?

  41. SECTION 4 How do we manage IT?

  42. The challenge ‘Delivering innovation faster’ Agility ‘New capabilities to support a changing business’ Functionality ‘Can you do it often enough? Capacity Does it behave as expected? Reliability Text

  43. UBS IT: Our Geographic Footprint EMEA801 Switzerland5601 UBS has 65,000 staff, 17,000 in IT, (5500 IT staff in the Investment Bank) UK2829 Americas3660 Russia .APAC4341 Japan Hong Kong Brazil Australia

  44. How do we manage multi-site/offshore development?

  45. Round the clock development in Singapore, Kiev, London, Stamford & Chicago People Methodology: Agile - Scrum - KanBan Controlled code - Shared code base – daily builds & automated unit testing QA teams sited with developers in all sites – Test Driven Development Clear Responsibilities, Communications and Process – good communication is essential Small components with integration challenge v monolithic applications Technology Communication, Training, Documentation even more important Using Vendors Formal Spec OR Have Vendor’s staff integrated into multi-site model How do you Test offshore teams code – or do they QA your own teams code?

  46. 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

  47. Scrum / KanBan Board @ UBS

  48. 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) Support global systems Change global systems Do this for less than it cost last year Strategic Investment (building/buying new, retiring old) Support future revenue & profitability growth Own and manage the entire IT spend

  49. UBS Investment Bank IT: Award-winning Technology 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 2001-2010Best Firm for Portfolio Trading Execution Thomson Reuters Extel 2008-2010 Best product innovation for Prime Broking Asiamoney 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 UBS Delta: Most Innovative Actuarial Software Life & Pension Risk 2010 UBS Delta: Best Technology Vendor for Risk ManagementCredit Awards 2009Technology Innovation AwardCredit Magazine 2009, 2010Best 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 FX McLagan/Z-Yen 2010 No.1 for Post Trade Services – North AmericaZ/Yen McLagan 2009Best in AlgorithmsAsianInvestor 2010

  50. 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 Activities Dragon 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, Australia EOWA 2009 Top 20 Employers in Asia Pacific The 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

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