4 core insurance functions primed to benefit from agentic AI
Agentic AI in insurance has potential to transform insurance. Read four use cases.
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The SMOTE method for generating synthetic data
The Synthetic Minority Over-sampling Technique (SMOTE) was created to address class-imbalance problems in machine learning algorithms. The idea is to oversample from the rare events prior to running a machine learning classification algorithm. However, at its heart, the SMOTE algorithm (Chawla et al., 2002) is essentially a way to simulate
Predicting Survivor winners with Python: Real-world modeling from 47 seasons of data
Using 47 seasons of Survivor data, this analysis explores what gameplay traits correlate with winning, applying Python and SAS Viya Workbench to build predictive models. While stats like challenge wins and voting accuracy help narrow down potential winners, the findings suggest that intangible social dynamics play the most decisive role.