1 The Casualty ty Actu tuarial Society ty is committe tted to - - PowerPoint PPT Presentation

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The Casualty ty Actu tuarial Society ty is committe tted to to adhering str trictl tly to to th the lette tter and spirit t of th the anti titr trust t laws. Seminars conducte ted under th the auspices of th the CAS are designed solely to to provide a forum for th the expression of various points ts

• f view on to

topics described in th the programs or agendas for such meeti tings.

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Under no circumsta tances shall CAS seminars be used as a means for competi ting companies or firms to to reach any understa tanding – – expressed or implied – – th that t restr tricts ts competi titi tion or in any way impairs th the ability ty of members to to exercise independent t business judgment t regarding matte tters affecti ting competi titi tion.

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It t is th the responsibility ty of all seminar parti ticipants ts to to be aware of anti titr trust t regulati tions, to to prevent t any writte tten or verbal discussions th that t appear to to violate te th these laws, and to to adhere in every respect t to to th the CAS anti titr trust t compliance policy. policy.

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} Background Information } Data } Uses } Strategies and Plans } Summary } Open Discussion/Questions

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} Art Cadorine, ISO } Pete Marotta, ISO } Tracy Spadola, Teradata

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1343: first formal policy written in Italy

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1494: double entry bookkeeping established

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1726: Sun Fire Office established

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1736: Union Fire (Ben Franklin) established

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1779: standard marine policy established

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1792: states started to impose limitations on company activities and investments

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1824: NY established a tax on premiums

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1828: Annual Sta tate tement t concept t is create ted in NY Y with th 13 cate tegories of questi tions

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1850: A & H coverage in US

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1851: states start insurance company examinations

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1853: NY Y annual report t expanded requires more data ta

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1871: Lloyds established

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1871: Nati tional Conventi tion of Insurance Commissioners (NCIC) – – fire and marine blank

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1873: MA adopts first standard fire policy

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1898: auto liability coverage

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1899: auto collision coverage

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1902: auto property damage coverage

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1911: first workers’ comp policy

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1911: NCIC model reserve law

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1922: Nati tional Council on Compensati tion Insurance esta tablished

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1922: NY Y law requires insurers to to file premiums and loss experience in conformance with th approved classificati tions

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1923: NY Y requires Casualty ty Ex Experience Ex Exhibit t

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1944: South East Underwriters case

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1945: McCarran Ferguson enacte ted by Congress

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1948: sta tate tes pass regulati tions/laws regarding sta tati tisti tical plans, rate tes and rules

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1949: Insurance Ex Expense Ex Exhibit t intr troduced

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1950: NAIC adopts ts multi ti-line blank

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1967: ACORD formed to create standardized

• perational forms

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1969: Schedule P changed to calendar/accident year basis

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1971: ISO formed from several national insurance service organizations

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1983: Insurance Value Added Network (IVANS); first batch processing via IVANS

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1995: EU directive on data protection

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1996: HIPAA and FCRA passed by Congress

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1996: Solvency II in EU

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1966: Graham-Leach-Bliley passed by Congress

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2002: Sarbanes-Oxley passed by Congress

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2003: CA data breach law

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} Michael Lesk (Network World, October 28,

2003)–

• including sounds and images there are

thousands of petabytes of information

• T.K. Landauer – “How much do people

remember?”, Cognitive Science, Oct/Dec 1986: the human brain holds 200 MB of information

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Kilobyte (KB) 1,000 bytes OR 103bytes 2 Kilobytes: A Typewritten page. 100 Kilobytes: A low-resolution photograph. Megabyte (MB) 1,000,000 bytes OR 106 bytes 1 Megabyte: A small novel OR a 3.5 inch floppy disk. 2 Megabytes: A high-resolution photograph. 5 Megabytes: The complete works of Shakespeare. 10 Megabytes: A minute of high-fidelity sound. 100 Megabytes: 1 meter of shelved books. 500 Megabytes: A CD-ROM. Gigabyte (GB) 1,000,000,000 bytes OR 109 bytes 1 Gigabyte: a pickup truck filled with books. 20 Gigabytes: A good collection of the works of Beethoven. 100 Gigabytes: A library floor of academic journals. Terabyte (TB) 1,000,000,000,000 bytes OR 1012 bytes 1 Terabyte: 50000 trees made into paper and printed. 2 Terabytes: An academic research library. 10 Terabytes: The print collections of the U.S. Library of Congress. 400 Terabytes: National Climactic Data Center (NOAA) database. Petabyte (PB) 1,000,000,000,000,000 bytes OR 1015 bytes 1 Petabyte: 3 years of EOS data (2001). 2 Petabytes: All U.S. academic research libraries. 20 Petabytes: Production of hard-disk drives in 1995. 200 Petabytes: All printed material. Exabyte (EB) 1,000,000,000,000,000,000 bytes OR 1018 bytes 2 Exabytes: Total volume of information generated in 1999. 5 Exabytes: All words ever spoken by human beings. Zettabyte (ZB) 1,000 EBs 8

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} University of California, San Diego

• June 2008 announced a 3 year study to quantify

the amounts and kinds of information being produced worldwide

• The “How Much Information?” study will be

completed by a multi-disciplinary, multi- university faculty team supported by corporate and foundation sponsorship

• http://giic.ucsd.edu

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} University of California, Berkeley

• “How Much Information 2003?”, senior researchers Peter

Lyman and Hal R. Varian

• Print, film, magnetic and optical:

– 5 exabytes in 2002 – 92% on magnetic media – Doubles every 3 years/quadruples in 2 years

• Telephone, radio, TV, Internet: 18 exabytes in 2002
• http://www2.sims.berkeley.edu/research/projects/how-

much-info-2003/

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Sto torage Mediu Medium 2002 2002 Terabyte tes Up Upper er Es Esti timate te 2002 2002 Terabyte tes Low Lower er Es Esti timate te 1999- 1999-200 200 0 Upper 0 Upper Es Esti timate te 1999- 1999-2000 2000 Low Lower er Es Esti timate te % C % Chan ange e Up Upper er Es Esti timate tes

Paper 1,634 327 1,200 240 36% Film 420,254 76,69 431,690 58,209

• 3%

Magnetic 5187130 3,416,230 2,779,760 2,073,760 87% Optical 103 51 81 29 28% TOTAL: 5,609,121 3,416,281 3,212,731 2,132,238 74.5% 11

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Mediu Medium 2002 Terabyte tes

Radio 3,488 Television 68,955 Telephone 17,300,000 Internet 532,897 TOTAL 17,905,340 12

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How much data ta is in your organizati tion – – and how has th this changed from th the 20th th to to th the 21st t centu tury? 
 
 Is th there any one single area th that t eith ther

• versees data

ta at t th the ente terprise level or is charged with th coordinati ting and documenti ting data ta acti tiviti ties ente terprise-wide?

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From insurance data managers to enterprise data managers, managing many types of data including insurance data

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Where in th the organizati tion should th the data ta management t functi tion be housed? Do Do you see th this changing in th the 21st t centu tury?

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} Climate change (NAIC survey) } Premium leakage } Fraud } Loss control } Predictive models } ERM

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What t new problems will data ta managers be asked to to address?

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} Optical/graphical:

• Satellite imagery, maps, photos
• Video: risk assessment, claims handling

} Voice: call centers, scripts, fraud detection } Cellular/telephonic:

• Embedding cellular connectivity
• RFIDs
• Telematics, EDRs
• Claims reporting via cell phone apps
• Loss control

} Internet

• For standard insurance functions
• Social media: communication and mining

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} Privacy: in sync with Chief Privacy Officer } C-level: in sync with business strategies } Quality

• Broader application to new uses of data,
• More emphasis on external and an enterprise

view

} Standards implementation

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As data ta privacy and confidenti tiality ty receive broader mandate tes – – what t role is th the data ta manager playing in regards to to prote tecte ted data ta? 
 
 And what t roles should/will data ta ma mana nage ger’s be playing in th the futu ture?

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} GIS } GPS } Traffic } Weather } Health/Medical } Pharma } Risk components

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The granularity ty of data ta – – desired and available - has exploded in th the past t few decades decades.
 
 How has th this explosion impacte ted data ta management? t? 
 
 Again what t is th the impact t on data ta management t best t practi tices?

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Wh What at’s next t in data ta?

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} Metadata repositories, data dictionaries, MDM, } ETL } Data profiling, audits and controls } Data and text mining } Entity resolution } Visualization } Longitudinal functionality } Encryption

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What t new to tools will data ta managers be u be usin ing?

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In oth ther industr tries – – th that t is oth ther th than P & C insurance – – data ta management t may be part t

• f IT. And over th

the past t few years th the data ta management t te term its tself has been borrowed by IT. by IT. 
 
 What t is th the relati tionship betw tween IT and data ta management t - histo torically and how do you see th this changing in th the futu ture?

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} Value of Predictive modeling dependent on Quality Information

responding to what is needed

• Underwriting
• Rating
• Claims administration
• Fraud detection and prevention
• Operations

} Reserving Opinions and being held legally responsible for the data

that is being used

} Ratemaking and adequate, responsive classification systems } Current not a year old to respond to changing situations } Catastrophe information

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} Sarbanes Oxley requirements } SEC Financial Reports } Solvency II requirements } Risk Based Capital Requirements

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} Increased automation } Accurate experience modifications } Individual insured pricing } Current market conditions } Proper exposure

• Premium leakage
• Pre-fill

} Claims management } Loss Control } Distribution channels/new markets

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Insurance data ta managers have lead th the data ta quality ty charge over th the years – – edit t packages, sta tati tisti tical plans, sta tandardized code lists ts, chief DQ DQ officers, for example.
 
 Is th this sti till tr true? How has data ta quality ty evolved and what t new data ta management t best t practi tices have been create ted to to respond to to th this evoluti tion?

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What t about t data ta quality ty/data ta management t measures? 
 
 Anyth thing new?

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} An alignment of Business Vision, Mission, Goals and

Initiatives to the underlying data and information of an

• rganization

} Requires an Understanding of:

• Your Direction in 18-24 months and in five years
• Industry direction in 18-24 months and in five years
• Opportunities for your organization
• Target benchmarks
• Data and Information

– Available – Needed – Data Gaps Da Data ta – – tr treate ted like all corporate te assets ts

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In In you your experien r experiences h ces how

• w com

common

• n is C-

is C-lev level el recogniti tion of data ta management? t? 
 
 Do Do th they perceive th this to to be an IT or business functi tion? 
 
 How pervasive is data ta governance and ste tewardship?

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} Facilitate alignment and traceability of significant IT

investments to their respective business drivers

• Provide a process and a set of tools to facilitate Business and

IT planning and decision-making

• Maintain a common and consistent view of data that is

shared company wide

• Aids good corporate governance and promotes data

transparency

} Poorly-managed data WILL result in faulty business decisions

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Data and information support corporate decision-making and provide competitive advantage

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Organizational level:

} Information Governance } Data Stewardship } Data Architecture } Data and Process Models } Training and Education

Data level :

} Data Element Management } Data Quality } Data Standards } Data Privacy & Security

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The Rules, Tools, and Schools… …

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} Strategic Data Planning is primarily a Business, not

an IT function.

• IT critical to any enterprise data strategy.

} Actuaries are uniquely positioned in an

• rganization - data savvy as data definers and

users, senior business level visibility, etc. – to be prime movers in Strategic Data Planning.

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} Establish a Corporate or Chief Data Steward } Foster data and data quality standards } Structure organization to promote good data

management and data quality

} Data flows from business processes } Manage DQ as close to the source as possible } Establish processes to maximize data quality and

utility

} Design and maintain data, systems and reporting

mechanisms in a manner that promotes good data management and data quality

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Some data ta quality ty experts ts, in and outs tside of th the insurance industr try, have observed th that t data ta quality ty is simply a first t ste tep in achieving overall ente terprise quality ty – – with th informati tion quality ty and presenta tati tion quality ty being ste teps tw two and th three.
 
 What t are your th thoughts ts on th this

• bservati

tion? What t should th the data ta ma mana nage ger’s role be in DQ DQ, IQ, and Presenta tati tion Quality ty?

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What t about t th the data ta manager’s s ro role le in in promoti ting ente terprise business inte telligence?

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} An understanding of risk exposures across and

beyond the organization

• Market Risk, Credit Risk, Operational Risk, as well as

Insurance Risk

– What impact does your investment portfolio have on your

• perations

– What impact does fluctuating currency have? – What risks are your key stakeholders subject to?

} Enterprise Risk Management brings in a “new” level

and source of data and information that needs to be managed

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The data ta manager’s s ro role le has has evo evolved lved fro from m th the ta tacti tical – – sta tati tisti tical reporti ting, special calls – – data ta-aggregati tion and reporti ting functi tions to to support t of core insurance functi tions such as u/w, claims administr trati tion, loss contr trol, premium audit, t, pricing, etc

• tc. 



 What t are th the next t ste teps in th this evoluti tion ?

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} Promote data governance within the

• rganization

} Define and follow enterprise data strategies } Support the interoperability of data within

the organization and with trading partners

} Metadata, metadata, metadata, … } Know and vet third party data resources

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} Control access to your granular data

resources

} Develop and implement comprehensive and

flexible data quality measures

} Remember that data management applies to

structured and unstructured data sets

} Require adherence to data management

best practices not only at the corporate level but also at the desk top level

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