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SWOC DAMA 2008 Showcase at American Modern Insurance February 21, 2008 SWOC DAMA 2008 Showcase at American Modern Insurance February 21, 2008

Showcase Agenda n Background/Business Case 20 minutes Sandy Wagner n Data Warehouse – AIIM Showcase Agenda n Background/Business Case 20 minutes Sandy Wagner n Data Warehouse – AIIM 20 minutes Latha Subramanian n Data Model – AIIM 20 minutes Duke Ganote n Information Management – AIIM 20 minutes Dan Daly n Q& A – Duke/Sandy/Latha/Dan 20 minutes

American Modern Insurance Company Background n Founded in 1938 as a consumer finance company American Modern Insurance Company Background n Founded in 1938 as a consumer finance company n Provider of highly focused, specialty insurance products n Positioned to grow into a multi-billion dollar organization n Entrepreneurial spirit & deep commitment of employees n Approximately 1200 employees country-wide, with 1000 employees in eastern Cincinnati area (Amelia)

American Modern Insurance Company Background The organization believes that the strategic deployment of technology American Modern Insurance Company Background The organization believes that the strategic deployment of technology can help it achieve, and sustain, a competitive advantage. n As stated in its Operating Principles, “Our investment in information technology is part of a carefully planned strategy to ensure that American Modern's company-wide infrastructure is among the most advanced in the specialty insurance industry. ” n

American Modern Insurance Initiative Background In 2000, American Modern embarked upon long-range initiative, coined American Modern Insurance Initiative Background In 2000, American Modern embarked upon long-range initiative, coined “modern. LINK, ” n n Business and IT collaboration Business case and funding Three prongs: n n n Web-enable insurance transaction processing Replace aging legacy processing systems Develop a Knowledge Management architecture

American Modern Insurance Business Case n The anticipated returns of this business case were: American Modern Insurance Business Case n The anticipated returns of this business case were: n 20% annual increases in directly-attributed new business n 37% of Policy and Partner Administration moved from existing internal units directly to point of service n 25% improvement in current Product Review and Management cycle time n 21% improvement in Product Filings cycle time n 2% reduction in total loss ratio directly attributed to modern. LINK initiative

American Modern Insurance Business Case n These returns would yield a significant recurring annual American Modern Insurance Business Case n These returns would yield a significant recurring annual benefit through additional premium, increased profit, and decreased expenses n Almost 50% of these benefits would be attained through better knowledge/data management, richer data segmentation, and improved data and risk selection n John Hayden, President and CEO, American Modern states: n We must have accurate data about the risks we insure today if we are to ever be successful in establishing The Right Rate for Every Risk we choose to insure in the future.

American Modern Insurance Knowledge Management Roadmap n Enterprise Data Model n Operational Data Store American Modern Insurance Knowledge Management Roadmap n Enterprise Data Model n Operational Data Store n Enterprise Data Warehouse n Themed analytic data marts n Enterprise reporting portal n Metadata management n Data Stewardship

American Modern Insurance Knowledge Management Results n Business users can: n n Respond quickly American Modern Insurance Knowledge Management Results n Business users can: n n Respond quickly to new business initiatives n n Make informed decisions Create new opportunities Business users are: n Moving from data collectors to data consumers n Asking “why” instead of “what”

American Modern Insurance Knowledge Management Results n Retention – Joe David. In the last American Modern Insurance Knowledge Management Results n Retention – Joe David. In the last four years, we have leveraged the corporate reporting tools to develop a series of targeted strategies that have allowed us to improve retention by nearly eight points, which equates to annualized premium of nearly $60 million n Claims. Integration of 3 rd party Claim data - Heather Bolyard. This one-month sample of data for one material has identified a potential indemnity reduction of $70, 000. n Reserving – Gene Stetler. The new Loss Reserving data store from the Enterprise Data Warehouse has enabled process efficiencies, thus allowing us to predict our reserving needs with accuracy. n Product – Kevin Randall. The implementation of American Modern's data warehouse has been a significant part of the successful launch of the company's right rate for every risk initiative

American Modern Insurance 2007 Awards and Recognition In 2007, American Modern received two awards American Modern Insurance 2007 Awards and Recognition In 2007, American Modern received two awards from Computerworld: n Laureate - The laureate status for the Enterprise Data Warehouse presented at the Carnegie Mellon Auditorium in Washington D. C – June 2007 n BI Award - Best Practices in Business Intelligence in the category “Creating an Agile BI Infrastructure” presented in Las Vegas, NV – September 2007

Showcase Agenda n Background/Business Case 20 minutes Sandy Wagner n Data Warehouse – AIIM Showcase Agenda n Background/Business Case 20 minutes Sandy Wagner n Data Warehouse – AIIM 20 minutes Latha Subramanian n Data Model – AIIM 20 minutes Duke Ganote n Information Management – AIIM 20 minutes Dan Daly n Q& A – Duke/Sandy/Latha/Dan 20 minutes

Enterprise Data Warehouse n Create an implementation roadmap n n n Implement “value” after Enterprise Data Warehouse n Create an implementation roadmap n n n Implement “value” after each iteration n n Content scope – January 1998 thru present All products loaded over 5 years Loss Cost, Retention, Loss Triangles Establish Data Stewardship - 2004

Enterprise Data Warehouse The data warehouse will support: Loss Cost Analysis Retention Analysis Product Enterprise Data Warehouse The data warehouse will support: Loss Cost Analysis Retention Analysis Product Pricing Analysis modern. LINK Reporting Data Warehouse Profitability Analysis Financial Analysis Underwriting Analysis

Data Warehouse Value SB Loss Cost MC Loss Cost Retention UVRC Pricing / GLM Data Warehouse Value SB Loss Cost MC Loss Cost Retention UVRC Pricing / GLM Loss Triangles modern. LINK MH PIF m. LINK vs. Legacy MH Loss Cost Renewal Reporting Address Data FID Retro Studies MSB CAT Analysis Partner Experience Reporting Agency Profile Analysis Mapping Cancellation Reporting Claims Liability

Data Warehouse Statistics 1997 policies used to seed warehouse: ~700, 000 Total policies Jan Data Warehouse Statistics 1997 policies used to seed warehouse: ~700, 000 Total policies Jan 1998 thru Jun 2007 Total units Jan 1998 thru Jun 2007 Average Number of Coverages per policy: 5 Average number of policies in-force per month: 800, 000 Average number of claims per month: 8, 000

Data Warehouse Benefits n Single version of the truth n Data integrated at the Data Warehouse Benefits n Single version of the truth n Data integrated at the lowest level n High-end hardware platform n Codes translated to “English” terms n Resolve source system problems n Data quality review and correction n Integration of external information

Data Mart Themes modern. LINK quote n Exposure n Retention n Experience n Loss Data Mart Themes modern. LINK quote n Exposure n Retention n Experience n Loss Cost n Claims n Underwriting n

Technology Enablers…. n IBM RS 6000 AIX processors n EMC data storage n Oracle Technology Enablers…. n IBM RS 6000 AIX processors n EMC data storage n Oracle DBMS n COGNOS for reporting utilizing query, report, mapping and analytical tools n Websphere Portal n LDAP for single sign-on

Showcase Agenda n Background/Business Case 20 minutes Sandy Wagner n Data Warehouse – AIIM Showcase Agenda n Background/Business Case 20 minutes Sandy Wagner n Data Warehouse – AIIM 20 minutes Latha Subramanian n Data Model – AIIM 20 minutes Duke Ganote n Information Management – AIIM 20 minutes Dan Daly n Q& A – Duke/Sandy/Latha/Dan 20 minutes

Data Model n n Provides a common, integrated way for the corporation to view Data Model n n Provides a common, integrated way for the corporation to view and to communicate about its business Allows the business to drive the system Creates standard definitions/documentation Provides structure to new development projects

Enterprise Data Model People Places Insureds Geography Operators Address Lienholders Claimants Things Quotes/Policies Claims Enterprise Data Model People Places Insureds Geography Operators Address Lienholders Claimants Things Quotes/Policies Claims Coverages Accidents/Violations Homes/Vehicles UW rules Makes/Models

Jump Start Enterprise Data Model Generic Model based on Insurance Industry Practices Acord Standards Jump Start Enterprise Data Model Generic Model based on Insurance Industry Practices Acord Standards m or sf an Tr Integrated View: Common Data Definitions Across business AMIG Specific Requirements Manufactured Home Site Built Motorcycle Motor Home Travel Trailer Classic Auto FID Commercial AMIG Enterprise Data Model

Data Model Benefits n n n Foundation for: n modern. LINK rate & quote Data Model Benefits n n n Foundation for: n modern. LINK rate & quote applications n Data warehouse/data mart/analytic design n m. LP 3 Operational Data Store (ODS) design New projects simply add to the model n Insurance score n Claims liability Development of data standards and a common “language”

Inmon, Initially n Data warehouse built using Inmon approach: Source (nonrelational) End of month Inmon, Initially n Data warehouse built using Inmon approach: Source (nonrelational) End of month Data Warehouse (normalized) End of month “Corporate Information Factory Components”, W. H. Inmon http: //www. inmoncif. com/view/26 Data. Mart (star)

Conformance Conformed Dimensions: Conformed Dimensions Data Warehouse (normalized) Retention Mart (star) Pricing Data. Mart Conformance Conformed Dimensions: Conformed Dimensions Data Warehouse (normalized) Retention Mart (star) Pricing Data. Mart (star) Loss Cost Data. Mart (star) “The 38 Subsystems of ETL”, Ralph Kimball http: //www. intelligententerprise. com/show. Article. jhtml? article. ID=54200319

Challenges n n n Multiple sources Latency Stewardship Challenges n n n Multiple sources Latency Stewardship

Multiple Sources OPPORTUNITIES: n n Daily claims/catastrophe feeds 3 rd party Claim data (claims Multiple Sources OPPORTUNITIES: n n Daily claims/catastrophe feeds 3 rd party Claim data (claims cost standards) Huon (an new Insurance ERP) Munich RE (pending merger with reinsurer)

Multiple Sources RESPONSES: n Pull data: generally from relational DBMS, e. g. DB 2, Multiple Sources RESPONSES: n Pull data: generally from relational DBMS, e. g. DB 2, Informix, SQL Server n Push data: generally from nonrelational DBMS: DMS II (Unisys)

Latency Changes OPPORTUNITY: Daily information n n Catastrophe reporting; e. g. Hurricane Katrina 2005, Latency Changes OPPORTUNITY: Daily information n n Catastrophe reporting; e. g. Hurricane Katrina 2005, “Fab Four” of 2004 Financial Institutions requesting daily account information on insureds.

Latency Changes n RESPONSE: Kimball architecture y ail d Source (OLTP) daily Daily Conformed Latency Changes n RESPONSE: Kimball architecture y ail d Source (OLTP) daily Daily Conformed Dimensions Staging Area da il y daily CATastrophe Data. Mart (star) “Kimball Design Tip #34: You Don’t Need an EDW”, Ralph Kimball http: //www. kimballgroup. com/html/designtips. PDF/Design. Tips 2002/Kimball. DT 34 You. Dont. Need. pdf

Latency Changes Kimball Architecture “The staging area is exactly like the kitchen in a Latency Changes Kimball Architecture “The staging area is exactly like the kitchen in a restaurant. The kitchen is a busy, even dangerous, place filled with sharp knives and hot liquids. The cooks are busy, focused on the task of preparing the food. It just isn't appropriate to allow diners into a professional kitchen or allow the cooks to be distracted with the very separate issues of the fine dining experience. ” Two Powerful Ideas: foundations for modern data warehousing, Ralph Kimball Sept 17, 2002: http: //www. intelligententerprise. com/020917/515 warehouse 1_1. jhtml

Data Stewardship OPPORTUNITY: Daily instead of monthly reference data needed. However, for example, no Data Stewardship OPPORTUNITY: Daily instead of monthly reference data needed. However, for example, no daily system of record automated for: n Claims Adjusters n Catastrophe name/details

Data Stewardship RESPONSE: n n n Data stewards maintain master data / system of Data Stewardship RESPONSE: n n n Data stewards maintain master data / system of record. Over night ETL uses master data for building dimension. Referential integrity always enforced with fact table, so data stewards cannot “delete” required for integrity.

Showcase Agenda n Background/Business Case 20 minutes Sandy Wagner n Data Warehouse – AIIM Showcase Agenda n Background/Business Case 20 minutes Sandy Wagner n Data Warehouse – AIIM 20 minutes Latha Subramanian n Data Model – AIIM 20 minutes Duke Ganote n Information Management – AIIM 20 minutes Dan Daly n Q& A – Duke/Sandy/Latha/Dan 20 minutes

Information Management Benefits n Single BI Architecture n Provides a consistent view of our Information Management Benefits n Single BI Architecture n Provides a consistent view of our Corporate Data n Allows for common product training & support n Volume license pricing provides flexibility and cost savings n Converting Data Collectors to Information Consumers n Corporate Portal Integration n Delivering specific information to specific business users n Providing pre-emptive alerts to users based on specific (data) events

Single BI Architecture (Consistent View, Common Training & Support & Volume Pricing) n Using Single BI Architecture (Consistent View, Common Training & Support & Volume Pricing) n Using Cognos 8. 2 for our Enterprise Reporting Portal n n Report Studio, Analysis Studio, Query Studio, Event Studio, Metric Studio All Cognos Content Provided in Themes n n n n modern. LINK quote Exposure Retention Experience Loss Cost Claims Underwriting

Single BI Architecture (Consistent View, Common Training & Support & Volume Pricing) Single BI Architecture (Consistent View, Common Training & Support & Volume Pricing)

Converting Data Collectors to Information Consumers n Corporate Portal Integration Converting Data Collectors to Information Consumers n Corporate Portal Integration

Converting Data Collectors to Information Consumers n Delivering specific content to specific users n Converting Data Collectors to Information Consumers n Delivering specific content to specific users n ‘Bursting’ Experience & Exposure information directly to our Business Partners (Agents)

Converting Data Collectors to Information Consumers n Providing pre-emptive alerts to users based on Converting Data Collectors to Information Consumers n Providing pre-emptive alerts to users based on specific (data) events

So What’s Next? n n Spend more time executing strategy & less time gathering So What’s Next? n n Spend more time executing strategy & less time gathering data Manage to Corporate Scorecards / Performance Metrics

Showcase Agenda n Background/Business Case 20 minutes Sandy Wagner n Data Warehouse – AIIM Showcase Agenda n Background/Business Case 20 minutes Sandy Wagner n Data Warehouse – AIIM 20 minutes Latha Subramanian n Data Model – AIIM 20 minutes Duke Ganote n Information Management – AIIM 20 minutes Dan Daly n Q& A – Duke/Sandy/Latha/Dan 20 minutes

Q & A session Q & A session

Wrap Up n Enterprise Data Warehouse now in its 7 th year n Business Wrap Up n Enterprise Data Warehouse now in its 7 th year n Business units embrace the DW n Holistic view of information in one place n Next phase: deliver similar functionality to our external business partners n Our case study has been placed in National Archives n The copy of the case study can be found on the following web page: http: //www. cwhonors. org/view. Case. Study. asp? Nominatio n. ID=54

SWOC DAMA 2008 Showcase at American Modern Insurance February 21, 2008 SWOC DAMA 2008 Showcase at American Modern Insurance February 21, 2008