A standard measure of risk and risk-value models
Management Science
Modified moment estimation for the two-parameter Birnbaum--Saunders distribution
Computational Statistics & Data Analysis
Gradient boosting trees for auto insurance loss cost modeling and prediction
Expert Systems with Applications: An International Journal
Financial early warning system model and data mining application for risk detection
Expert Systems with Applications: An International Journal
A scoring model to detect abusive billing patterns in health insurance claims
Expert Systems with Applications: An International Journal
Selecting prospects for cross-selling financial products using multivariate credibility
Expert Systems with Applications: An International Journal
Operations Research Letters
Optimal customer selection for cross-selling of financial services products
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
A risk measure is a mapping from the random variables representing the risks to a number. It is estimated using historical data and utilized in making decisions such as allocating capital to each business line or deposit insurance pricing. Once a risk measure is obtained, an efficient monitoring system is required to quickly detect any drifts in the risk measure. This paper investigates the problem of detecting a shift in value at risk as the most widely used risk measure in insurance companies. The probabilistic C control chart and the parametric bootstrap method are employed to establish a risk monitoring scheme in insurance companies. Since the number of claims in a period is a random variable, the proposed method is a variable sample size scheme. Monte Carlo simulations for Weibull, Burr XII, Birnbaum-Saunders and Pareto distributions are carried out to investigate the behavior and performance of the proposed scheme. In addition, a real example from an insurance company is presented to demonstrate the applicability of the proposed method.