HSBC deploys AI tech to track money laundering

HSBC is working with data start-up Quantexa to tackle money laundering

Banking giant HSBC has begun to use artificial intelligence technology from big data start-up Quantexa to track and combat money laundering.

After completing a successful pilot of the technology, HSBC confirmed that it is planning to integrate Quantexa technology into its infrastructure by the end of the year.

The software harvests internal, publicly-available and transactional data from a customer's wider network in a bid to identify signs of money laundering.

Having conducted the first trials in 2017, HSBC will work with Quantexa to deploy its technology across the bank's global operations in a bid to 'better detect potentially illegal activity in its broader context'.

At the start of 2017, London-based Quantexa raised $3.3 million in an investment round led by Albion Ventures and HSBC.

CEO Vishal Marria said the firm is "honoured to be working with HSBC in their mission to combat money laundering".

He added: "Our market-leading technologies will be supporting the bank to join the dots of all their data to give a broader understanding of their customers and transactions across the globe.

"Through a better understanding, HSBC will be better equipped in their fight against financial crime."

Ray O'Brien, global risk COO and head of global risk analytics at HSBC, explained that the bank is "continuously looking for ways to build on our existing capabilities to detect and prevent financial crime".

He said: "Following our investment in Quantexa, we are looking forward to working closely with the company to utilise its technologies as we become more intelligence-led in our approach to financial crime risk management."

Suman Nambiar, head of AI Practice at Mindtree, said this technology will allow the bank to meet the demands imposed by regulators and streamline operations.

"By employing AI technology, HSBC can not only effectively comply with regulations but the human workforce is also freed to work on more strategic tasks," said Nambiar.

"Due to the complexity of financial transactions coupled with growing data sets, disparate transaction data systems, and the integration issues involved with monitoring systems, successfully tackling money laundering and complying with stringent regulations goes beyond human capabilities.

"Artificial intelligence and machine learning have a key role to play in addressing these challenges and keeping pace with the changing banking environment.

"This technology allows financial institutions to move beyond traditional business models to those dynamically adaptive predictive models, which enable real-time, customer-centric anomaly detection."