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  1. Minimum Bias, GLMs, and Credibility in the Context of Predictive Modeling
    When predictive performance testing, rather than testing model assumptions, is used for validation, the needs for detailed model specification are greatly reduced. Minimum bias models trade some degree of statistical independence in data …
  2. PEBELS: Policy Exposure Based Excess Loss Smoothing
    PEBELS is a method for estimating the expected loss cost for each loss layer of an individual property risk regardless of size. By providing maximum resolution in estimating layer loss costs, PEBELS facilitates increased accuracy and sophistication …
  3. Data & Technology Working Party Report
    The evolving definition of Advanced Analytics and the emergence of the Data Scientist In its infancy, Actuarial Science operated at the leading edge of contemporary analytic capabilities and could be easily said to be employing “advanced analytics.…
  4. Hierarchical Compartmental Models for Loss Reserving
    Motivation This paper proposes a triangle-based stochastic reserving framework for parsimoniously describing insurance claims generation, reporting and settlement processes with intuitive parameters. Method Deterministic compartmental models …
  5. Judgmental Topics In P&C Companies: Findings from a Prediction Survey
    Interaction with actuarial models by both actuaries and non-actuaries is inevitable and requires careful study so that these models may better serve their purpose. Yet empirical and scientific investigations into how experts make their judgments are …

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Predictive Modeling OC

The CAS Roundtable

Posted on 05/31/2017
By Rick Gorvett

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