  We consider the problem of efficient financial surveillance aimed at
"on-the-go" detection of structural breaks (anomalies) in "live"-monitored
financial time series. With the problem approached statistically, viz. as that
of multi-cyclic sequential (quickest) change-point detection, we propose a
semi-parametric multi-cyclic change-point detection procedure to promptly spot
anomalies as they occur in the time series under surveillance. The proposed
procedure is a derivative of the likelihood ratio-based Shiryaev-Roberts (SR)
procedure; the latter is a quasi-Bayesian surveillance method known to deliver
the fastest (in the multi-cyclic sense) speed of detection, whatever be the
false alarm frequency. We offer a case study where we first carry out, step by
step, statistical analysis of a set of real-world financial data, and then set
up and devise (a) the proposed SR-based anomaly-detection procedure and (b) the
celebrated Cumulative Sum (CUSUM) chart to detect structural breaks in the
data. While both procedures performed well, the proposed SR-derivative,
conforming to the intuition, seemed slightly better.
