This post originally appeared on MIT Technology Review
The Nasdaq stock market is an attractive target for fraudsters. As the world’s largest stock exchange by volume, it must be constantly monitored for attempts to illicitly beat the system. These can include manipulations to inflate a stock’s closing price; churning (rapidly buying and selling stocks) to give the false impression of a lot of activity; and spoofing (placing a large buy or sell order with no intention of actually executing) to create artificially high demand.
That monitoring is now being aided by artificial intelligence, Nasdaq, the stock market’s parent company, announced today. A new deep-learning system is working in tandem with human analysts to keep watch over roughly 17.5 million trades per day.
The system augments an existing software surveillance system that uses statistics and rules to flag any signs of market abuse. In the US equity market, for example, the old system issued around 1,000 alerts per day for human analysts to investigate, says Martina Rejsjo, the head of market surveillance for Nasdaq’s North America equities. Only a fraction of these cases would subsequently be confirmed as fraud and result in heavy fines.
The new system should have a number of advantages. First, Nasdaq claims it will be more accurate at identifying patterns of abuse, reducing the burden on human analysts. Second, it will be better at detecting more complex patterns of abuse, particularly spoofing, which Nasdaq believes will become increasingly common.
To start, the system has been trained to detect particular subsets of abuse by learning from historical examples. Every time it detects similar suspicious activity, it will alert a human analyst with the appropriate expertise. Strange behavior in a biotech stock, for example, will immediately be flagged for an analyst familiar with the market behaviors of the biotech industry.
After investigating the case, the analyst enters the outcome back into the system. In this way, the deep-learning algorithm continuously refines its understanding. It will also be trained to detect different types of abuse over time.
But neural networks, the algorithms that power such deep-learning systems, are only as good as the examples they are trained on. In other domains, hackers have been able to fool them by exploiting their blind spots. Doug Hamilton, Nasdaq’s managing director of artificial intelligence, says that’s why the team will first roll out the new surveillance system on top of the old one, rather than replacing it immediately. Having human analysts as a backstop adds an additional layer of insurance, he says.
If the system is a success, the company plans to roll it out globally. Nasdaq also operates 29 total markets across North America and Europe and provides market surveillance technologies to 59 other marketplaces, 19 regulators, and over 160 banks and brokers. The firm sees clamping down on fraudulent activity as a vital part of maintaining trust in the financial system. “Market integrity is one of the most important things for an exchange,” says Rejsjo.
Crucially, the system should be able to adapt more quickly to new patterns as fraudsters’ tactics change and become more sophisticated.
The way they try to cheat the market is constantly evolving, says Tony Sio, Nasdaq’s head of marketplace regulatory technology, “so the patterns and types of abuse that are happening are constantly evolving as well.”