Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to his/her own past trading history and on the present trading activity of his/her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids.

Then, on the basis of a positive future that you like the look of, your brain works to adapt reality to match it: you do what needs to be done in order to end up in that rewarding position you imagined. Everyone in the space of artificial intelligence has known for a long, long time that the gap between where we are and human level intelligence is somewhat vaster than one might be forgiven for believing… they just don't go to any particular effort to highlight it as it could, well, turn down the money taps. Being so far from human level intelligence doesn't mean that AI (and particularly the field of machine learning) has not enhanced our lives, because it has. It is revolutionising the way we interact with machines, enabling automation that we couldn't have dreamed of just a few years ago and is making great strides into areas like medical research, drug discovery, disease prevention and treatment that are hard to ignore. But we should separate the component parts in this field that deliver real things from the dream of what researchers call "artificial general intelligence" (AGI): digital what-if machines that can, amongst other things, generalise their knowledge to adapt to new situations, imagine the rewards of future options to make wiser decisions now and have a sense of "self" that would allow us to treat them as some form of intellectual equal. To stick to current deep learning techniques and their artificial neural networks on the basis that "we'll get there, folks" is naive and no serious research into general intelligence is doing so.