  We describe a computational feather-light and intuitive, yet provably
efficient algorithm, named HALFADO. HALFADO is designed for detecting
suspicious events in a high-frequency stream of complex entries, based on a
relatively small number of examples of human judgement. Operating a
sufficiently accurate detection system is vital for {\em assisting} teams of
human experts in many different areas of the modern digital society. These
systems have intrinsically a far-reaching normative effect, and public
knowledge of the workings of such technology should be a human right.
  On a conceptual level, the present approach extends one of the most classical
learning algorithms for classification, inheriting its theoretical properties.
It however works in a semi-supervised way integrating human and computational
intelligence. On a practical level, this algorithm transcends existing
approaches (expert systems) by managing and boosting their performance into a
single global detector.
  We illustrate HALFADO's efficacy on two challenging applications: (1) for
detecting {\em hate speech} messages in a flow of text messages gathered from a
social media platform, and (2) for a Transaction Monitoring System (TMS) in
FinTech detecting fraudulent transactions in a stream of financial
transactions.
  This algorithm illustrates that - contrary to popular belief - advanced
methods of machine learning need not require neither advanced levels of
computation power nor expensive annotation efforts.
