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

In this insightful presentation by Daniel Finnegan, President of ISO Innovative Analytics, we explore the integration of Generalized Linear Models (GLMs) with data mining techniques. The discussion highlights the strengths of data mining in discovery while addressing its weaknesses in estimation. Finnegan emphasizes the importance of using data mining inputs as super variables within GLMs and dispels common myths in data mining and modeling. Best practices and diagnostic measures are shared to optimize model performance and decision-making in underwriting and loss management.

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

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Presentation Transcript

  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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