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A health insurance company in Germany aims to predict sick payments (Krankengeld) for its policyholders based on historical data
The objective of this analysis is to predict the Contribution Margin (DB) per Insurance Day (VT) using machine learning techniques for a health insurance company.
The Customer Churn Predictive Model forecasts customer attrition using historical data, enabling proactive retention strategies
The project describes an empirical investigation of the temporal course of coding certain diagnoses by ambulatory physicians in relation to the morbidity structure of insured persons.
In the statutory health insurance system in Germany, budgets are agreed upon between statutory health insurance funds and outpatient physicians for a portion of the services provided by the physicians.
This comprehensive regression methodology ensures accurate determination of standardized service expenditures
The available data includes risk characteristics and damage information related to motor liability insurance contracts.
A client X, a health Insurance company, maintains contact with physicians who have given the company opt-in consent via email communication.
A healthcare provider in Germany wanted to understand attitudes and behaviors to attract, retain, and engage consumers. A deeper understanding of consumers’ decision-making processes can also help attracting new customers.