Artificial intelligence, machine learning, and other cognitive disciplines keep finding secure toeholds in risk management, but experts doubt that the industry will see a big bang adoption of the technology. <\/p>\n
Were going to see a continual shift, and that is what weve seen for the past few years, said Josh Sutton, vice president and global head of data and artificial intelligence at Sapient. <\/p>\n
The technologys presence will make itself apparent across the front-, middle-, and back offices in fashions as different as the organizations themselves. <\/p>\n
In the near term, Sutton foresees most of AIs advancement in the middle- and back offices wrapped in the cloak of greaterrobotic process automation adoption. <\/p>\n
Josh Sutton, Sapient <\/p>\n
RPA effectively hit a wall over the past decade in that it could only handle the automation of activities that were 100% rules-based, said Sutton. The intersection of AI technologies with a lot of legacy automation work has enabled the ability to handle processing of things with a little bit more ambiguity, such as detecting and pro-actively addressing potential illegal trading behavior before the regulators do. <\/p>\n
One firm, which Sutton declined to name, is using RAP\/AI to allocate its compliance teams investigation resources. <\/p>\n
The organizations compliance system couldtag the transactions of a trader who made a series of out of the ordinary trades on a day when the trader received a significant number of external phone calls for further investigation. <\/p>\n
The AI could look at the incident and use its common sense to understand it was the traders birthday and that they were reasonable trades, explained Sutton. Im not going to investigate, and Im not going to prioritize that for an investigation. If that continues, Ill raise it back up to the top of the stack and let our team work on it. But initially, I would not waste my teams time on that. <\/p>\n
Similarly, the technology also handles the ambiguity of correlation within credit risk when firm wants to determinethe knock-on effects of a company melting down or defaulting. <\/p>\n
Thats always been the realm of economists and statisticians to come up with answers to that, he said. Thats an area where I think machine learning can leverage a lot of insight from historical data to come up with improved models. <\/p>\n
Although Sutton has not seen a lot of firms do that yet, but he hasnt been involved in that type of project personally, he said. <\/p>\n
Where Suttondoesnt see AI making that much of a difference would be in areas that already highly automated like risk management in the front office. <\/p>\n
This has been highly automated for a while, whether it is calculating value-at-risk or Comprehensive Capital Analysis and Review reports, said Sutton. <\/p>\n
A more disruptive technology likely will be the real-world deployment of quantum computing, he added. What used to take hours of computational processing from our existing systems can be done in seconds with quantum computing. I think that will be a bigger game changer in calculating market risk than AI. <\/p>\n
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Wall Street Saunters into AI - Markets Media (press release) (registration) (blog)<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Artificial intelligence, machine learning, and other cognitive disciplines keep finding secure toeholds in risk management, but experts doubt that the industry will see a big bang adoption of the technology. Were going to see a continual shift, and that is what weve seen for the past few years, said Josh Sutton, vice president and global head of data and artificial intelligence at Sapient. The technologys presence will make itself apparent across the front-, middle-, and back offices in fashions as different as the organizations themselves Continue reading