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Combing GLM and Data Mining Techniques Daniel Finnegan, President ISO Innovative Analytics

Combing GLM and Data Mining Techniques Daniel Finnegan, President ISO Innovative Analytics. The Punch Line. Data mining is good at discovery, weak at estimation Therefore, use data mining inputs as super variables in GLM’s. Data Mining Myths. No assumptions No need for expert input

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Combing GLM and Data Mining Techniques Daniel Finnegan, President ISO Innovative Analytics

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  1. Combing GLM and Data Mining TechniquesDaniel Finnegan, PresidentISO Innovative Analytics

  2. The Punch Line • Data mining is good at discovery, weak at estimation • Therefore, use data mining inputs as super variables in GLM’s

  3. Data Mining Myths • No assumptions • No need for expert input • One method (ours) is best in for all problems • New method (ours) overcomes all weaknesses of other methods

  4. GLM Myths • Mechanically calculates rates • All purpose solution

  5. Best Practices in Data Mining • Start with experts; • Data • Relationships • Issues • Decompose, analyze • Split sample • Use multiple methods • Reverse engineer results

  6. Best Practices in GLM • Model Structure of Problem • Decisions: • Underwriting vs. Loss • Allowable Choices • Trend • Development Factors • Correlated Losses (Cat) • Structure of Policy • Limits • Deductibles

  7. Best Practices in GLM (Continued) • Use Extensive Diagnostics • Assumptions Tests • Measures of Fit • Subgroup Analysis • Range of Decisions

  8. Combing GLM and Data Mining TechniquesDaniel Finnegan, PresidentISO Innovative Analytics

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