  We develop a monitoring procedure to detect changes in a large approximate
factor model. Letting $r$ be the number of common factors, we base our
statistics on the fact that the $\left( r+1\right) $-th eigenvalue of the
sample covariance matrix is bounded under the null of no change, whereas it
becomes spiked under changes. Given that sample eigenvalues cannot be estimated
consistently under the null, we randomise the test statistic, obtaining a
sequence of \textit{i.i.d} statistics, which are used for the monitoring
scheme. Numerical evidence shows a very small probability of false detections,
and tight detection times of change-points.
