Oleksandr Romanko, Ph.D. Senior Research Analyst, Risk Analytics Business Analytics, IBM Canada October 8, 2013 Business Analytics and Optimization
Please note: IBM Risk Analytics statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here. 2
About me Dr. Oleksandr Romanko Research Analyst, Quantitative Research at Risk Analytics, Business Analytics, IBM, with the company since 2010 Ph.D. in Computer Science from McMaster University Author of over 10 papers and reports Lecturer at University of Toronto and McMaster University Research areas: operational research, optimization, finance portfolio optimization, multi-objective optimization market and credit risk modeling and optimization numerical methods for risk management design of numerical algorithms and their software implementation 3
Quick facts about IBM Risk Analytics (formerly Algorithmics Inc.) Algorithmics offers risk solutions, software and advisory services for Banking Insurance Asset Management Hedge Funds Pension Funds Founded in 1989 Acquired by IBM in 2011 Over 800 employees worldwide 200 in Research and Development 250 in Professional Services 110 in Business Lines Head Office in Toronto Primary offices in London and New York 23 offices globally in all major financial centers Clients in 55 countries 2010 revenue over $163M USD 4
Some of our clients 5
Being an IBMer
IBM Centennial: A Century of Progress 2011 1911 Incorporated on June 16, 1911 in US as the Computing Tabulating Recording Company CTR changed its name to International Business Machines Corporation globally in 1924 CTR changes name in Canada to International Business Machines Company in 1917 7
Making the world work better pioneering the science 1969 1973 1981 2008 8
IBM Centennial: 100 Years of Innovation 9
Risk Analytics
Simulation modeling example 1 You are planning for retirement and decide to invest $1000 in the US stock market for the next 30 years. Your initial capital is Invest into the S&P 500 market index (index fund) Between 1977 and 2007, S&P 500 returned 8.79% per year on average with a standard deviation of 14.65% Assume that every year your investment returns from investing into the S&P 500 will follow a Normal distribution with the historical mean and standard deviation. Value of investment after 30 years: The return over 30 years will depend on the realization of 30 random variables Compute and plot 11
Simulation modeling example 1 Simulate 100 observations for each of 30 single period returns 6 x 104 Simulated Value Paths 40 5 35 30 4 25 Value 3 2 Frequency 20 15 10 1 5 0 0 5 10 15 20 25 30 Time 0 0 1 2 3 4 5 6 Value after 30 years x 10 4 12
Simulation modeling example 1 Simulate 5000 observations for each of 30 single period returns 14 x 104 Simulated Value Paths 500 12 450 400 10 350 Value 8 6 Frequency 300 250 200 4 150 2 100 50 0 0 0 5 10 15 20 25 30 0 2 4 6 8 10 12 14 Time Value after 30 years x 10 4 Number of values 5000 Mean $ 12,587.62 Std Deviation $ 10,948.39 Skewness 3.349066 Kurtosis 28.24214 Mode $ 4,458.97 5% Perc $ 2,655.55 95% Perc $ 32,481.38 Minimum $ 609.75 13 Maximum $194,355.00
Simulation modeling example 2 You are planning for retirement and decide to invest in the market for the next 30 years. Your initial capital is You have an opportunity to invest in stocks and Treasury bonds: allocate 50% of your capital to the stock market (S&P 500 index fund) today allocate 50% of your capital to bonds today Assume that every year your investment returns from investing into the S&P 500 and Treasury bonds will follow a Normal distribution with the mean and standard deviation as in example 1 (for S&P 500), mean 4% and standard deviation 7% for bonds. Assume correlation -0.2 between the stock market and the Treasury bond market. Covariance matrix: Value of investment after 30 years: 14
Simulation modeling example 2 Simulate 5000 observations for each of 30 single period returns 8 x 104 Simulated Value Paths 1200 7 6 5 1000 800 Value 4 3 Frequency 600 400 2 1 200 0 0 0 5 10 15 20 25 30 0 1 2 3 4 5 6 7 8 Time Value after 30 years x 10 4 Number of values 5000 Mean $ 7,892.80 Std Deviation $ 5,233.10 Skewness 2.921482 Kurtosis 20.48869 Mode $ 5,050.96 5% Perc $ 2,951.82 95% Perc $17,457.43 Minimum $ 1,408.63 15 Maximum $79,729.34
Which risks are worth taking? 16
Distribution of losses and sampling Portfolio loss distribution: 1,000 samples 10,000 samples Probability Probability Loss Loss 17
Computing risk measures from simulation Portfolio Value-at-Risk (VaR) Based on empirical distribution e.g. VaR 99% over 1000 scenarios 10 th worst outcome 18
Risk reporting VaR and P&L 19
Portfolio credit risk optimization Credit losses 99%-Value-at-Risk Optimal Portfolio 99%-Value-at-Risk Initial Portfolio 20
Optimization in IBM Algo Risk Application 21
Business Analytics
Business analytics and optimization
What is analytics? Analytics is the scientific process of deriving insights from data in order to make decisions Analyze Decide Data Insight Action Descriptive Analytics What has happened? Predictive Analytics What will happen? Prescriptive Analytics What should we do? Business Value 24
IBM Business Analytics portfolio IBM Business Analytics Industry Solutions Financial Services Public Sector Distribution Industrial Communications Functional Solutions Customer Acquisition Customer Lifetime Value Customer Social Media Analytics Customer Loyalty & Retention Budgeting & Forecasting Finance Financial Consolidation Sales Performance Management Disclosure Management Profitability Modeling & Optimization Resource Optimization Production Planning Operations Asset Management Decision Management Risk Risk Identification Risk Mitigation Planning Risk & Control Assessment Risk Aware Decisioning Core Capabilities REPORT ANALYZE MODEL PLAN COLLABORATE PREDICT Visualize Discover Simulate Govern Contribute Forecast Mine Score Survey Decide Software Categories Business Intelligence Predictive Analytics Performance Management Risk Analytics 25
Operations research Operations Research (O.R.) is the discipline of applying advanced analytical methods to help make better decisions Analytical techniques: Simulation giving you the ability to try out approaches and test ideas for improvement Optimization narrowing your choices to the very best when there are virtually innumerable feasible options and comparing them is difficult Probability and Statistics helping you measure risk, mine data to find valuable connections and insights, test conclusions, and make reliable forecasts Mathematical Modeling algorithms and software 26
Our planet is a complex, dynamic, highly interconnected $54 Trillion system-of-systems (OECD-based analysis) This chart shows systems (not industries ) Electricity $ 2.94 Tn Education $ 1.36 Tn Communication $ 3.96 Tn Water $ 0.13 Tn Transportation $ 6.95 Tn Leisure / Recreation / Clothing $ 7.80 Tn Global system-of-systems $54 Trillion (100% of WW 2008 GDP) Healthcare $ 4.27 Tn Infrastructure $ 12.54 Tn Note: 1. Size of bubbles represents systems economic values 2. Arrows represent the strength of systems interaction Source: IBV analysis based on OECD Finance $ 4.58 Tn Food $ 4.89 Tn Govt. & Safety $ 5.21 Tn 1 Tn Legend for system inputs Same Industry Business Support IT Systems Energy Resources Machinery Materials Trade 27
Economists estimate, that all systems carry inefficiencies of up to $15 Tn, of which $4 Tn could be eliminated This chart shows systems (not industries ) Improvement potential as % of system inefficiency 40% 35% 30% 25% 20% Analysis of inefficiencies in the planet s system-of-systems Electricity 2,940 Food & Water 4,890 Building & Transport Infrastructure 12,540 Financial 4,580 Communication 3,960 Transportation (Goods & Passenger) 6,950 Leisure / Recreation / Clothing 7,800 34% Education 1,360 System inefficiency as % of total economic value Healthcare 4,270 42% Government & Safety 5,210 Note: Size of the bubble indicate absolute value of the system in USD Billions 15% 15% 20% 25% 30% 35% 40% 45% Global economic value of System-ofsystems Inefficiencies Improvement potential $54 Trillion 100% of WW 2008 GDP $15 Trillion 28% of WW 2008 GDP $4 Trillion 7% of WW 2008 GDP How to read the chart: For example, the Healthcare system s value is $4,270B. It carries an estimated inefficiency of 42%. From that level of 42% inefficiency, economists estimate that ~34% can be eliminated (= 34% x 42%). 28 Source: IBM economists survey 2009; n= 480
Business Analytics Examples
Smarter Cities 30
We can collect information from almost everything to make better decisions 30 billion RFID tags embedded into our world and across entire ecosystems 1 billion Camera phones in existence able to document accidents, damage, and crimes 85% Of new automobiles will contain event data recorders collecting travel information Instrumented Interconnected Intelligent 31
Police use analytics to reduce crime 32
Marketing and supply chain analytics 33
Intelligent transport systems Real time monitoring & forecasting of congestion in cities enables real time action to reduce traffic and emissions Can charge drivers at point of use for access to city centers Stockholm Congestion Tax Project Involves 18 barrier-free control points Allows differentiated pricing by time of day, congestion level, and potentially emissions level Results: Traffic reduced by 100,000 vehicle passages per day (25%) Public transportation passengers increased by 40,000 / day Congestion during peak hours and CO 2 emissions were dramatically reduced 34
Artificial intelligence 35 Source: A Brief Overview and Thoughts for Healthcare Education and Performance Improvement by Watson Team
Artificial intelligence In May 1898 Portugal celebrated the 400th anniversary of this explorer s arrival in India. On On 27th 27th May May 1498, 1498, Vasco Vasco da da Gama Gama On landed 27th May landed in in Kappad 1498, Vasco Beach Beachda Gama landed in Kappad Beach On the 27 th of May 1498, Vasco da Gama landed in Kappad Beach Search Far and Wide Explore many hypotheses celebrated Portugal Find Judge Evidence Many inference algorithms landed in May 1898 400th anniversary Temporal Reasoning 27th May 1498 arrival in Statistical Paraphrasing Date Math India GeoSpatial Reasoning Paraphrases Kappad Beach Geo- KB explorer Vasco da Gama 36 Source: A Brief Overview and Thoughts for Healthcare Education and Performance Improvement by Watson Team
Artificial intelligence 37
Visual Analytics
Visual analytics Visual statistics of the Napoleon Campaign: the Minard Map 39
Visual analytics 40
Visual analytics portfolio 41
Historical visualization Activity Histogram Track Summary Heat Map 42 Distribution of events over time Show tracks of all objects returned from search How long objects spent in different places
Master of Business Master of Business Analytics
Master of Business Analytics programs top 20 universities
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Master of Business Analytics programs curriculum Прикладная статистика и теория вероятности (Applied Statistics and Probability) Основы вычислительной математики (Fundamentals of Computational Mathematics) Интеллектуальный анализ данных и эксперные системы (Data Mining and Knowledge Discovery) Имитационное моделирование (Simulation Modelling) Принятие решений в финансах (Financial Decision Making) Вычислительные методы для анализа бизнес данных (Computational Methods for Business Data Analysis) Вычислительные финансы и риск менеджмент (Computational Finance and Risk Management) Визуальная аналитика и представление знаний (Visual Analytics and Knowledge Representation) Математическое моделирование в бизнесе (Mathematical Modelling for Business) Аналитика в маркетинге (Marketing Analytics) Стратегии менеджмента инноваций (Strategies for Managing Innovations) Аналитика Интернета, социальных сетей и бизнес новостей (Analytics of Web, Social Networks and Business News)
Master of Business Analytics programs curriculum
Online Education
Online education 49
Online education 50
Questions 51
Thank you for your attention Contact Information: Oleksandr Romanko Senior Research Analyst, Quantitative Research Risk Analytics Business Analytics, IBM oleksandr.romanko@ca.ibm.com romanko@romanko.ca http://www.romanko.ca 52
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Decision Making and Decision Making and Risk Management
Decision making Lesson: know who makes the decisions and what her/his objective is Did risk management fail in these crises? Financial Crisis BP Oil Spill Volcanic Eruption 55
BP oil spill 56