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Reserving with Machine Learning: Applications for Loyalty Programs and Individual Insurance Claims
Motivation Reserving is typically performed on aggregate claim data using familiar reserving techniques such as the chain ladder method. Rich data about individual claims is often available but is not systematically used to estimate ultimate losses. …
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 …
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 …
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.…
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 …
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