Elsemary/Actuarial-Reserve-Risk-Classification-with-Gaussian-Mixture
Classification of reserve risk with chain-ladder
Applies Gaussian Mixture Models with expectation-maximization to classify loss triangle cells into risk categories, using log-transform preprocessing to handle lognormal-distributed claims data. Generates synthetic data for missing triangle values while preserving chain-ladder structural constraints through stratified grouping across demographic and claim-type dimensions. Built with scikit-learn and pandas, targeting actuarial reserve estimation workflows.
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MIT
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Aug 31, 2019
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