# Detecting Suspicious Events in Fast Information Flows

arXiv: https://arxiv.org/abs/2101.02424 · HTML: https://mlsanity.com/paper/2101.02424/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [On the optimality of the Hedge algorithm in the stochastic regime](https://mlsanity.com/paper/1809.01382/) (2018)
- [FADO: A Deterministic Detection/Learning Algorithm](https://mlsanity.com/paper/1711.02361/) (2017)

## Abstract (arXiv)

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.