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Danish investigators to build artificial intelligence exposing stock-market fraud

Danish investigators build artificial intelligence to publicize stock-market fraud

The Department of Engineering at Aarhus University is heading a fresh project to insert a finish to stock-exchange fraud and market manipulation. The investigators will apply artificial intelligence AI to clean up the extensive fraud taking place, where control is currently implemented via manual sampling.

A team of investigators starting with Aarhus University AU has just received a grant of a few million DKK starting with the Independent investigation Fund Denmark for a project that may affect share trading throughout the globe.

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The team is being headed by Alexandros Iosifidis, an associate professor at the Department of Engineering and an expert in machine learning. The project aims to build a system that has to identify suspicious trading on all of the globe’s stock exchanges—something that is currently being done through manual samples.

The AI resolutions we are watching for need far less manual work, they reduce costs, and they are much extra operational than today’s controls, says Alexandros Iosifidis.

As yet, no one knows the level of stock-market fraud universally, however, the US Federal Trade Commission FTC has estimated that, in the US alone, the problem amounts to a good number of billions of dollars per year.

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There are many varieties of stock-market fraud, which is a ticking bomb beneath the whole philosophy behind the market-economy principle of supply and demand.

A reliable, automatic safeguard against market manipulation could, therefore, be crucial for transparent stock markets, says Alexandros Iosifidis.

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The project will plan entirely fresh methods which, applying machine learning, will be able to conduct systematic analyses of stock-exchange activity, recognize irregular transactions, and identify configurations in share-price data. This will mark it realistic to detect, and even predict, market manipulation in share trading across the entire globe.

We’re working on the supposition that trading in one share affects future trading in other shares in exact patterns that have to be recognized by applying data-driven analyses. We’re concentrating on recording trading activity based on jumps in regular share prices, and we will notice these in actual stock-exchange data in order to identify relationships between share transactions and irregular trading activities, he says.

The project is called Data-driven Inter-Stock Predictive Analytics or DIS, and it will run for three years, coordinated by Associate Professor Alexandros Iosifidis.

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