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Artificial intelligence (AI) has become an essential resource for financial institutions. Certain core operations couldn't function without process automation, and AI it can enable faster, more personalised financial services. But alongside these advantages in a competitive market are a range of legal challenges, including data protection concerns, the algorithmic bias and the systemic risks in greater reliance on automation. Whilst no large financial institution can afford not to integrate AI into its business, it's important to make the parameters of AI deployment transparent and open to scrutiny.
AI uses probabilistic, rather than deterministic, decision-making logic. This means that it draws inferences from the presence of data points which it has been programmed to identify within a sample of material, rather than following the rules of causation that humans often use. If a large mass of existing data is structured so that a computer can 'learn' from it, AI can spot patterns across millions of data inputs. The distinguishing feature of AI from other kinds of automation, however, is that it can alter its analysis of the data if given feedback on whether its inferences were correct.
Data is the fuel of AI, but the drive to accumulate, structure and analyse information in 'data pools' presents the ever-increasing risk of large-scale data theft. The damage, reputational and otherwise, of such incidents may be compensable when the information concerns passwords, but an increasing proportion of the data which banks store about their customers can be described as 'inherent'. This includes fingerpri
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Rory is a solicitor in the Financial Regulation team at Pinsent Masons LLP, with personal academic interests in private, public and international law approaches to emerging financial technologies.
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