Session 3 Big Data and Insurance Industry. Prof. Jack Ching Syang Yue, ASA

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1 Session 3 Big Data and Insurance Industry Prof. Jack Ching Syang Yue, ASA

2 2015/06/25 Big Data and Insurance Industry Speaker: Jack C. Yue National Chengchi Univ. Date: June 25,

3 Summary What is Big Data? Attributes of Big Data Limitations of Big Data Big Data and Insurance Future of Big Data 3 What is Big Data? The term Big Data was first proposed by IBM in 2010, and it involves three V s: Volume: size at least TB or PB Variety: including visual and GIS data Velocity: real time (instant) analysis Note: According to IDC (International Data Corporation), the capacity of data storage grows 50% annually. 4 2

4 The Growing Size of a Computer File! 5 6 3

5 7 Information Explosion! Source:The Expanding Digital Universe, A Forecast of Worldwide Information Growth Through 2010, March 2007, 8 An IDC White Paper - sponsored by EMC IDC predicted in 2007 the storage will grow sixfold: EB EB (Pred.) Note: International Data Corporation (IDC) 4

6 The Explosion Starting in 2007 IDC updated (in 2009) the prediction to tenfold from 2006 to 2011: EB EB EB (pred.) EB (pred.) Source:The Diverse and Exploding Digital Universe, An Updated Forecast of Worldwide Information Growth Through 2011 March 2008, An IDC White Paper - sponsored by EMC 9 The Era of Big Data! Data accumulation speed is much faster 10+ years ( ) to complete the Human Genome Project, sequencing 3 billion letters (base pairs), and now it takes only one day. More than 70% of the stock shares traded in 2010 (NYSE + NASDAQ) are generated from Automatic Trade System (or Statistical Trading System). (30,000 trades in NYSE every second). Facebook has 10 million plus pictures uploaded every hour and more than 3 billion like everyday. (User data size 300 PB) 10 5

7 Attributes of Big Data 11 Value of Information In the late 1990 s, Amazon hired more than 10 book critics to give recommended lists. But, the sales of books from readers suggestions are much better. (At least 1/3 sales are from the automatic system.) Wal-mart used historical data in 2004, and found that the sales of flash light and Pop-Tarts increase before the hurricane. (Note: Diaper and Beer is another well-known example.) 12 6

8 About Wal-Mart Wal-mart was one of the first companies to collect and analyze customers data, and use them to increase sales. During the weekend, people who purchase diapers tend to purchase beer as well. Question: Why are they purchased together and how can use this information to stimulate the sales? Adding Value to the Data A famous example in Data Mining:$$$ Strategies of marketing and warehouse Milk, eggs, sugar, bread Milk, eggs, cereal, bread Eggs, sugar Customer1 Customer2 Customer3 7

9 Value of Diaper and Beer Diaper and beer is a good example of Association, instead of Causality. Association can create value from the data, not necessarily via finding the causality. In the case of diaper and beer, we can plan: Pricing and marketing; Goods layout and floor design: Warehouse and Inventory. Attributes of Big Data In addition to the size, big data also have the following attributes: Sample = Population Non-uniform Data with Noise Association vs. Causality 8

10 Sample = Population (n = all?) We can use the whole population and don t need to infer from sample data. (Bias?) More updated information (c.f. Census); Remove possible sampling bias; Prevent insufficient sample. But we need to consider the problems of storage, updating, and analysis. Sample Representative 9

11 Sampling Bias!! 19 Non-uniform Data with Noise In addition to its voluminous and non-uniform nature, big data tend to have noises. Sources and formats create the problem of data compatibility. (Meta Analysis!) Need to modify in-homogeneity. Volume is more important (c.f. quality) CPI official statistics vs. Commodity prices from web (Deflation after Lehman Brothers' bankruptcy) 01/the-5-but-really-the-6-mistakes- made-in-big-data/ _sept253-jpg/ 10

12 Association vs. Causality Quantitative analysis alone cannot decide the causality, and there may exist latent variables. (e.g., foot size and spelling ability of grade school students) Complete data and powerful tools make finding association possible (pre-caution). UPS started Preventive Maintenance in New York City used the manhole records to predict (defective) explosions. (2010 Wired) United Parcel Service (UPS) was one of the earliest adopters of business analytics in Big Data. Photographer: David Paul Morris/Bloomberg 22 11

13 Limitations of Big Data 23 Statistics and Knowledge Statistics (& quantitative tools) can be classified as Induction, and help us to identify: Regular Irregular Extreme 12

14 Induction Deduction Information or Junk Explosion Information is everywhere but what are the really important factors? What information is essential? Data quality? (Garbage in, garbage out!) How the information is used for decision? (e.g., avoid high risk decisions, reduce the possible risk.) 13

15 Data Information Fact Knowledge Trial and Error The size of big data makes it difficult to create SOP for analysis, and the traditional Trial-and-error can be used. Data scientists at Kaggle found that the best bet is an orange used car. (odd color vs. selfexpression of car owner??) In the prediction of NYC defective manholes (using 2008 to predict 2009), the correction rate is 44%. (Key factor: year cable made) 14

16 29 About Data Collection The data usually are collected via: 1. Experimental Design 2. Observational Study The major difference is if the collection would influence the outcomes. (e.g., Observing the stock and housing markets usually won t distort the results.) Note: We shall focus on association. 15

17 Causality is not obvious! In pricing insurance products, we always look for risk factors. Age and gender are two well-known factors, and theories are proposed for explanation. Marriage is also a potential factor, and possible reasons are selection-at-marriage, responsibility, living arrangement (& reciprocal care giving), and social interaction. 32 Taiwan s Age-specific Mortality Rates ( ) 16

18 log(qx) Married Single Divorced log(qx) Married Single Divorced Age Age Taiwan s Marital Mortality Rates (Female) 33 Big Data and Insurance through-enterprise-it/analyze-this-big- Data-is-insurance-against-losing-acompetitive/ba-p/143577#.UgZmpLQVEqQ 34 17

19 Big Data and Insurance Industry In 2009, Google analyzed keywords used in the search engine (3 billion records daily). Comparing to the 2003~08 records in CDC, Google can detect the spread of H1N1 at least one week earlier. (User feedback!) John Snow (1854) studied the under-water system of London and found that the spread of Cholera is related to polluted water. (Spatial Statistics) New Territory of Insurance Industry? Big Data is insurance against losing a competitive edge.. Insurance companies face more diverse risk, in addition to interest risk. Living longer and more information create new possibilities and new risks. (e.g., longevity risk & moral hazard) Note: We shall use the health insurance products as a demonstration. 18

20 Insurance related Big Database In addition to the experienced data from individual insurance companies, public data are also available: Mortality Study: Human Mortality Database (HMD) and Ministry of Interior (Taiwan). Health Data: National Health Insurance (Taiwan), Society of Actuaries (SOA), and United States Renal Data System (USRDS). Taiwan s National Health Insurance Taiwan started the national health insurance (NHI) in 1995, and more than 99% population are covered (excluding oversea works). Waiver of copayment is one of the important policy in NHI. In addition to veterans, pregnant women, and people in remote areas, Catastrophic Illness (CI) patients also enjoy the copayment waiver. CI patients (4% population) spend 30% of total cost in

21 Handling Big Data The size and quality of NHI database make data analysis difficult. Need to rely on database software and data scientists (e.g., IT experts). Data cleaning is a big issue, especially the health care data are from different hospitals. Data Discrepancy? The death records are not complete in NHI database, and many are even wrong!

22 Cleaning the Data It is difficult to handle the big data using regular software and the database software (e.g., SQL) is required. Data cleaning and exploratory data analysis (EDA) are the key to success. For example, more than one databases are available and there exist discrepancy. Note: ID Incidence; HV_CD Mortality 41 The Future of Big Data content/uploads/2013/05/screen-shot at AM.png 42 21

23 Things to be Considered To incorporate with big data, the insurance industry needs to consider: Obtaining and updating data: confidential issue in a company; data sharing between companies (property right) Maintenance and usage: Safety and privacy (data cloud?); Institutional Review Board Data and meta analysis: Industry-academic cooperation; R&D Human Protection in Human Research What, Why, How, When and Where The purpose of IRB review is to assure, both in advance and by periodic review, that appropriate steps are taken to protect the rights and welfare of humans participating as subjects in the research. IRBs use a group process to review research protocols and related materials (e.g., informed consent documents and investigator brochures) to ensure protection of the rights and welfare of human subjects of research. 22

24 Suggestions from Other Users Barry Ralston (Assistant VP of Data Management at Infinity Insurance) suggests: Analyze data that matters. As you store, so you retrieve. Improve performance at the origin. Time-to-decision matters. Get ahead of the game. Note: Ralston says Data is key to what we do. 45 My Suggestions for using Big Data In addition to actuaries, insurance companies also need experts in big data (& information technology), such as data scientist/statistician. Data are an important asset, and regulating the data trading would become necessary. Let the users of big data bear the burden of privacy issue

25 Big Data = Opportunity? The profitability is always a issue. (Use the sale of LTC as an example.) 47 LTC Insurance in U.S.A. The main reasons why the sales of LTC insurance in U.S.A. are not good: Low Consumer Demand, Pricing, and Managing the Risk. The LTC insurance is associated to long term risk and not easy to handle ALM: Long-tail Liability Risk; Cash Flow (Asset Management) Note: Low interest rate and low withdrawal rate 48 would increase the premiums. (10%~40%). 24

26 Factors Influencing LTC Sales Asset Investment Strategies e.g., Convertible Bonds, Derivatives, Collateralized Loan Obligations, Private Placements Liability Risk Transfer e.g., Claim Securization, Commission Securization, Offshore Reinsurance, Product Redesign Regulatory Impacts on LTC Risk Management e.g., Principle-based Approach in U.S. & Solvency II Regulations in Europe Source: Long Term Care Insurance Section (SOA) 49 Thank you for your Attention! Q & A 50 25

2. Professor, Department of Risk Management and Insurance, National Chengchi. University, Taipei, Taiwan, R.O.C. 11605; jerry2@nccu.edu.

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