When Complex Event Processing (CEP) emerged around ten years ago, one of the early applications was real-time risk management. In the financial sector, there was growing recognition for the need for real-time visibility – continuous calibration of positions – in order to keep pace with the emerging importance of algorithmic trading. This is now relatively well-established in banking and trading sectors; Chemitiganti argues that the insurance industry now faces similar requirements.
In 2008, Chris Martins, then Marketing Director for CEP firm Apama, suggested considering CEP as a prospective “dog whisperer” that can help manage the risk of the technology “dog” biting its master.
But “dog bites master” works in both directions. In the case of Eliot Spitzer, the dog that bit its master was the anti money-laundering software that he had used against others.
And in the case of algorithmic trading, it seems we can no longer be sure who is master – whether black swan events are the inevitable and emergent result of excessive complexity, or whether hostile agents are engaged in a black swan breeding programme. One of the first CEP insiders to raise this concern was John Bates, first as CTO at Apama and subsequently with Software AG. (He now works for a subsidiary of SAP.)
|from Dark Pools by Scott Patterson|
And in 2015, Bates wrote that “high-speed trading algorithms are an alluring target for cyber thieves”.
So if technology is capable of both generating unexpected events and amplifying hostile attacks, are we being naive to imagine we use the same technology to protect ourselves?
Perhaps, but I believe there are some productive lines of development, as I’ve discussed previously on this blog and elsewhere.
1. Organizational intelligence – not relying either on human intelligence alone or on artificial intelligence alone, but looking for establishing sociotechnical systems that allow people and algorithms to collaborate effectively.
2. Algorithmic biodiversity – maintaining multiple algorithms, developed by different teams using different datasets, in order to detect additional weak signals and generate “second opinions”.
Vamsi Chemitiganti, Why the Insurance Industry Needs to Learn from Banking’s Risk Management Nightmares.. (10 September 2016)
Theo Hildyard, Pillar #6 of Market Surveillance 2.0: Known and unknown threats (Trading Mesh, 2 April 2015)
Neil Johnson et al, Financial black swans driven by ultrafast machine ecology (arXiv:1202.1448 [physics.soc-ph], 7 Feb 2012)
Chris Martins, CEP and Real-Time Risk – “The Dog Whisperer” (Apama, 21 March 2008)
Scott Patterson, Dark Pools – The Rise of A. I. Trading Machines and the Looming Threat to Wall Street (Random House, 2013). See review by David Leinweber, Are Algorithmic Monsters Threatening The Global Financial System? (Forbes, 11 July 2012)
Richard Veryard, Building Organizational Intelligence (LeanPub, 2012)
The Shelf-Life of Algorithms (October 2016)