Oleksandr Romanko, Ph.D. Senior Research Analyst, Risk Analytics Business Analytics, IBM Canada October 8, Business Analytics and Optimization

Transcription

1 Oleksandr Romanko, Ph.D. Senior Research Analyst, Risk Analytics Business Analytics, IBM Canada October 8, 2013 Business Analytics and Optimization

2 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

3 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

4 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

5 Some of our clients 5

6 Being an IBMer

7 IBM Centennial: A Century of Progress 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

8 Making the world work better pioneering the science

9 IBM Centennial: 100 Years of Innovation 9

10 Risk Analytics

11 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

12 Simulation modeling example 1 Simulate 100 observations for each of 30 single period returns 6 x 104 Simulated Value Paths Value 3 2 Frequency Time Value after 30 years x

13 Simulation modeling example 1 Simulate 5000 observations for each of 30 single period returns 14 x 104 Simulated Value Paths Value 8 6 Frequency Time Value after 30 years x 10 4 Number of values 5000 Mean $ 12, Std Deviation $ 10, Skewness Kurtosis Mode $ 4, % Perc $ 2, % Perc $ 32, Minimum $ Maximum $194,355.00

14 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

15 Simulation modeling example 2 Simulate 5000 observations for each of 30 single period returns 8 x 104 Simulated Value Paths Value 4 3 Frequency Time Value after 30 years x 10 4 Number of values 5000 Mean $ 7, Std Deviation $ 5, Skewness Kurtosis Mode $ 5, % Perc $ 2, % Perc $17, Minimum $ 1, Maximum $79,729.34

16 Which risks are worth taking? 16

17 Distribution of losses and sampling Portfolio loss distribution: 1,000 samples 10,000 samples Probability Probability Loss Loss 17

18 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

19 Risk reporting VaR and P&L 19

20 Portfolio credit risk optimization Credit losses 99%-Value-at-Risk Optimal Portfolio 99%-Value-at-Risk Initial Portfolio 20

21 Optimization in IBM Algo Risk Application 21

22 Business Analytics

23 Business analytics and optimization

24 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

25 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

26 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

27 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 $ 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

28 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

29 Business Analytics Examples

30 Smarter Cities 30

31 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

32 Police use analytics to reduce crime 32

33 Marketing and supply chain analytics 33

34 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

35 Artificial intelligence 35 Source: A Brief Overview and Thoughts for Healthcare Education and Performance Improvement by Watson Team

36 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 th 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

37 Artificial intelligence 37

38 Visual Analytics

39 Visual analytics Visual statistics of the Napoleon Campaign: the Minard Map 39

40 Visual analytics 40

41 Visual analytics portfolio 41

42 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

43 Master of Business Master of Business Analytics

44 Master of Business Analytics programs top 20 universities

45 Industry support for Master of Business Analytics programs

46 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)

47 Master of Business Analytics programs curriculum

48 Online Education

49 Online education 49

50 Online education 50

51 Questions 51

52 Thank you for your attention Contact Information: Oleksandr Romanko Senior Research Analyst, Quantitative Research Risk Analytics Business Analytics, IBM 52

53 Legal Disclaimer IBM Corporation All Rights Reserved. The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this publication, it is provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. Nothing contained in this publication is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. 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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. If the text includes any customer examples, please confirm we have prior written approval from such customer and insert the following language; otherwise delete: All customer examples described are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics may vary by customer. Please review text for proper trademark attribution of IBM products. At first use, each product name must be the full name and include appropriate trademark symbols (e.g., IBM Lotus Sametime Unyte ). 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54 Decision Making and Decision Making and Risk Management

55 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

56 BP oil spill 56