TM Exposure Data Quality and Catastrophe Modeling Rick Anderson February 28, 2002

© 2002 Risk Management Solutions, Inc. Data Quality Issues q Is the insurance to value being accurately reflected? q Does my data capture my actual exposure on a regional and peril basis? q Do I understand the default assumptions in my data? q Do I know that the information my brokers and agents are providing me is correct? qAm I capturing my aggregate information correctly?

© 2002 Risk Management Solutions, Inc. Statement of the Problem qWhat is the impact of poor data quality on: –Exposure data values –Uncertainty in modeled losses –Business decisions (external and internal) qHow do I quantify / score data quality –On a location basis –On a policy basis –On an aggregate portfolio basis qHow do I optimize data quality given my current business constraints? qWhat improvements should I be making?

© 2002 Risk Management Solutions, Inc. Tackling the Problem qClose working relationship with business partners –Agents –Reinsurers –Modeler qDevelopment of a structured data quality assessment qAbility to identify specific data quality issues and their impact on portfolio risk assessment at all levels. qDevelopment of a consistent independent data quality measure –Data Quality Index (DQI)

© 2002 Risk Management Solutions, Inc. Data Quality in the Context of Data Flow Primary Insurance Company Perspective Pricing, Reinsurance, Cap. Allocation, etc. Exposure Database Production Stream Cat Model Analysis Data Acquisition (Source Data) Data Resolution Analysis Process/ Operational Accuracy Analysis Data Acquisition Accuracy Analysis What does it mean? What matters? DATA QUALITY DATA FLOW

© 2002 Risk Management Solutions, Inc. Components of Data Quality qAccuracy component qResolution component

© 2002 Risk Management Solutions, Inc. Data Accuracy

© 2002 Risk Management Solutions, Inc. Examining the Components of Exposure Data Quality: Data Accuracy  How accurately is my data being captured and processed?  How accurately is my data being captured and processed? qExamination of processes through interviews and exposure data queries –Data acquisition –Data processing –Operations / systems qMarket dependent qLogic tree assessment framework

© 2002 Risk Management Solutions, Inc. Data Accuracy Components qData acquisition (source of data origination) –Conditional on type of source and line of business –Source reputation / bias –Source data vintage / validity / consistency / interpretability qData processing –Conditional on line of business –Bias / vintage / validity / consistency / interpretability qOperations / systems –Data accessibility / data integration / systems process / operations value to cost

© 2002 Risk Management Solutions, Inc. Accuracy Component of Data Quality Assessment Framework 1. Data Acquisition 2. Data Processing 3. Operations Accuracy Component Data Quality Questionnaire 1 Questionnaire 3 Questionnaire 2 W3W3 W2W2 W1W1 Warning flags from queries of the exposure database Peril and LOB specific On-Site Questions Components of Data Flow

© 2002 Risk Management Solutions, Inc. Example Logic Tree with Data Accuracy Criteria Data Acquisition Accuracy Score Reputation/Bias Data Reputation Bias Vintage Validity Consistency Interpretability Question 1 Question2... Question 11 Question Question 29 Question Direct Independent Agent Wholesale Broker Retail Broker Risk Retention Group Integrated Data Submission Catastrophe Model EDM Digital (Spreadsheet, Word Doc, etc.) Paper Submission

© 2002 Risk Management Solutions, Inc. Development of Data Accuracy Criteria Relative Importance Weights qAssessed as relative impact on modeled losses and key data quality issues qBased on: –Extensive interviews with Cat managers, underwriters and systems personnel –Results of relative parameter impact analyses on AAL (data validity criteria) –Availability of other information from which to draw assumptions qLine of business, peril, and region dependent

© 2002 Risk Management Solutions, Inc. Data Accuracy Criteria Development of Questionnaire qQuestionnaire is administered through interview process qQuestions are multiple choice –Yes / No –Always / Most of the Time / Occasionally / Never qNumber and content of questions designed to adequately assess how criteria are addressed at company qNormalized relative importance weighting applied to questions within each criteria

© 2002 Risk Management Solutions, Inc. Warning Flags Summaries from DB Queries qUsed as supporting information in answering questionnaire qWarning flags –Data consistency Address entryAddress entry ValuesValues Construction and occupancy class/schemaConstruction and occupancy class/schema –Data vintage –Data bias Secondary characteristicsSecondary characteristics Primary characteristicsPrimary characteristics

© 2002 Risk Management Solutions, Inc. Warning Flags Summaries from DB Queries – Sample Results Data Vintage – Use of Policy Status Flag Status# of Policies% of Total Policies BOOK116.7% No Status 583.3% Data Consistency – Value Entry Check Address TotalTotalAverage MatchLocationsValue Value MatchLocationsValue Value Street Level 5 $1,270,000$254,020 Zip Level 1 $147,500$147,500

© 2002 Risk Management Solutions, Inc. 1. Data Acquisition Accuracy qConditional on type of provider of data, data format, submission process, and line of business qData acquisition accuracy components –Validity –Vintage –Data provider bias –Data provider reputation –Consistency –Interpretability

© 2002 Risk Management Solutions, Inc. Acquisition Criteria: Relative Importance qData vintage qLocation validity checks qDefault value treatment qData acquisition bias qData validity checks qUse of data alteration flags qData aggregation qLocation entry consistency qReputation of data provider qSecondary construction characteristics treatment High Low

© 2002 Risk Management Solutions, Inc. 2. Data Processing qConditional on database format, platform, and line of business qIncorporates results from queries of exposure database qData processing accuracy components –Bias –Validity –Interpretability –Vintage –Consistency

© 2002 Risk Management Solutions, Inc. 3. Systems / Operations Accuracy qProcessing / operations data quality components –Data accessibility and storage –Data integration and linking –Technology systems process/flow –Operational value-to-cost

© 2002 Risk Management Solutions, Inc. Data Accuracy - Summary qAssessment of how closely processes arrive at the true and accepted value qStructured and consistent approach qAbility to assess the contribution of individual components to overall data accuracy score qPeriodic assessment is valuable for internal process review qIntegral component to overall data quality

© 2002 Risk Management Solutions, Inc. Data Resolution

© 2002 Risk Management Solutions, Inc. Examining the Components Of Exposure Data Quality: Data Resolution qWhat data am I capturing and at what level? qDirect query of exposure data parameters –Geocoding –Construction –Occupancy –Year built –Building height –Construction modifiers qPeril, region and market dependent

© 2002 Risk Management Solutions, Inc. Data Resolution Analysis Tree - California Earthquake Residential Cladding HAZARD VULNERABILITY Coordinate Zip Code County Location Resolution Const. Scheme Occupancy Class Secondary Characteristics Construction Class Year BuiltNumber of Stories Frame Bolted Down Soft Story Unknown URM Chimney Cripple Walls Unknown Inventory RMS ISO Fire Known Unknown Known Unknown MFW Frame SFW Frame SF House MF Housing LOCATION ATC

© 2002 Risk Management Solutions, Inc. Florida HU – Location Sampling (10 km. Grid)

© 2002 Risk Management Solutions, Inc. Florida HU – Location Sampling (1 km. Grid)

© 2002 Risk Management Solutions, Inc. Development of Category Weights qWeights for individual categories are determined through numerical simulation (analysis) of the impact of a given category on losses for the geography, peril, and LOB under consideration qFinal weights are normalized across the applicable categories CategoryHighMed.Low CategoryHighMed.Low Geocodingw 1a w 1b w 1c Cons. Schemew 2a w 2b w 2c Year Builtw 5a w 5b w 5c 2 nd. Char.w 6a w 6b w 6c qExtensive testing, validation, and benchmarking performed

© 2002 Risk Management Solutions, Inc. Florida HU – Scoring Regions

© 2002 Risk Management Solutions, Inc. California EQ – Scoring Regions

© 2002 Risk Management Solutions, Inc. Resolution Geocoding Scores by Hazard Level California Earthquake Residential

© 2002 Risk Management Solutions, Inc. Data Resolution Category Score Summary – California Earthquake Residential High Hazard Region

© 2002 Risk Management Solutions, Inc. Data Resolution Category Weights 0%10%20%30%40%50%60%70%80%90%100% High Med Low Seismic Regions Score (%) Geocoding Construction Scheme Construction Class Occupancy Class Year Built Secondary Chars. California Earthquake Residential

© 2002 Risk Management Solutions, Inc. Data Resolution Category Weights Florida Hurricane Commercial 0%10%20%30%40%50%60%70%80%90%100% Very High High Med Low Hazard Regions Score (%) Geocoding Construction Scheme Construction Class Occupancy Class Number of Stories Secondary Chars.

© 2002 Risk Management Solutions, Inc. Data Resolution - Aggregation Methodology Progression of Data Resolution Scoring Account 1 Account 2 Account 3 Commercial Portfolio Location Portfolio P1P1P1P1 Score A1A1A1A1 A3A3A3A3 A2A2A2A2 L1L1L2L2LnLnL1L1L2L2LnLn L1L1L2L2LnLnL1L1L2L2LnLn L1L1L2L2LnLnL1L1L2L2LnLn

© 2002 Risk Management Solutions, Inc. Data Resolution - Development of Relative “Importance” Factors qRelative importance is an approximation of the AAL. qGround-up AAL approximated at ZIP code level based on insurance industry exposure. qGross AAL approximated by average ratio of gross / ground-up AAL per attachment point.

© 2002 Risk Management Solutions, Inc. Layer contribution to AAL

© 2002 Risk Management Solutions, Inc. Sample Resolution Scores Company C Company B Company A Score (%) Geocoding Construction Scheme Construction Class Occupancy Class Number of Stories Secondary Chars.

© 2002 Risk Management Solutions, Inc. Improving Data Resolution – Leveraging Account Data Resolution Scores qIdentify accounts with score less than than target score qDetermine account potential for improvement as: (Score Difference) * (Account Importance) qIdentify accounts with biggest improvement potential and decide on strategy for data improvement

© 2002 Risk Management Solutions, Inc. Improving Data Resolution – Targeting Accounts qScore Difference = Target Score - Account Score qPotential Improvement = (Score Difference) * (Importance)

© 2002 Risk Management Solutions, Inc. Combining the Components

© 2002 Risk Management Solutions, Inc. Options for Combining Accuracy and Resolution Components qKeep separate qAdditive qMultiplicative qMinimum