The world of finance is changing ever more quickly, thanks to the emergence of new technologies. Some of these changes are advantageous; the accelerated adoption of machine learning and AI is increasing efficiencies in risk and assessment, underwriting and claims processing. But technological developments also pose risks to financial institutions. Professor Fabrizio Lillo’s work with Consob, the Italian financial authority, is using machine learning for good, to detect instances of market abuse.

There are several different kinds of market abuse, loosely defined as the circumstances in which an investor in the financial market has been unreasonably disadvantaged directly or indirectly by others through their behaviour. These include price manipulation, spoofing and front running.

Lillo’s work focuses on instances where traders use information about a listed company that is not publicly available, a kind of market abuse known as insider trading. If traders are made aware of confidential information about a company in advance, it is then relatively easy for them to trade in a way that guarantees a profit. Typically, insider trading occurs around what is known as “price sensitive events” when a company makes an announcement – say, the appointment of a new CEO or a takeover bid. Access to this kind of information in advance of its announcement is strictly prohibited but it can occur, causing some investors to make larger, unfair profits in comparison to their peers. Instances of insider trading can be penalised throughout the world, but it’s often difficult to identify them. (more…)