Artificial Intelligence: rise of robots could trigger banking risk
03 Nov 2017

The growing use of artificial intelligence (AI) needs to be more closely monitored as it could lead to financial firms becoming reliant on companies that are unsupervised, a global financial monitoring group has warned.

AI and machine learning technology are being used across the financial services sector, replacing the need for workers to physically carry out tasks such as assessing customers or identifying complex patterns.

This, however, could lead to firms becoming dependent on third parties, which in turn could “lead to the emergence of new systemically important players that could fall outside the regulatory perimeter,” the Financial Stability Board (FSB) found in its study of AI and machine learning in financial services.

The FSB also found that it will be important to assess the new technology in the light of areas such as adherence to rules regarding data privacy, conduct risks and cybersecurity.

In a London speech on robo-advice, Bob Ferguson of the UK Financial Conduct Authority’s strategy department said managing risk is ultimately the responsibility of the firm and its senior management.

“This responsibility isn’t reduced if the firm uses third party suppliers to help with the technology part – it rests with the firm offering the system. And if, for example, your method of profiling the risk appetite of the client is flawed, it is no more defensible because you sourced the model externally.”

In addition to the challenges presented by the new technology, the FSB study also highlighted some of the positive impacts of the robo-sphere, saying ‘the applications of AI and machine learning by regulators and supervisors can help improve regulatory compliance and increase supervisory effectiveness.

Some regulators are already using AI for fraud, anti-money laundering and counter terrorism funding detection.

The Australian Securities and Investments Commission has been exploring it in the area of identifying entities of interest from evidentiary documents, and the Monetary Authority of Singapore is exploring the use of AI and machine learning in the analysis of suspicious transactions.

“Investigating suspicious transactions is time consuming and often suffers from a high rate of false positives, due to defensive filings by regulated entities. Machine learning is being used to identify complex patterns and highlight the suspicious transactions that are potentially more serious and warrant closer investigation,” the FSB said.

-By Irene Madongo

Related topics:

The effects of artificial intelligence on the AML landscape

What makes good conduct regulation?

Why the traditional model of money laundering is obsolete

HSBC, AI startup Ayasdi team to fight money laundering

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