HSBC, the global banking and financial services, has selected Featurespace, the machine learning and adaptive behavioural analytics fraud prevention company, as its strategic provider to bolster its fraud mitigation capabilities.
HSBC will be using Featurespace’s ARIC™ platform to support efforts to strengthen anti-money laundering and fraud efforts in the insurance and retail fields respectively.
HSBC joins a number of other prominent financial institutions currently using Featurespace’s ARIC platform, including Worldpay, ClearBank and TSYS, among others.
Understanding the maturing role of graph databases in the enterprise
These institutions are leveraging Featurespace’s adaptive behavioural analytics and machine learning capabilities to monitor transactions, automatically detect anomalies in individual behaviour, and prioritise alerts and predict future threats.
“In the fight against the ever-evolving threat of financial crime, collaboration is key,” said Martina King, CEO at Featurespace. “These firms’ application of our technology will help them in optimising the detection of suspicious activity, ultimately protecting their customers from the global impact of financial crime.”
Financial crime stats
Spotting financial crime is currently an onerous and inefficient task for banks using outdated rules-based systems. Featurespace says. For example, that some systems ued by banks can create as many as 250,000 monthly financial crime alerts. According to figures from Featurespace, of those, nearly 1% (2,250) could be escalated and require human investigation (a suspicious activity report or “SAR”) and each SAR can take about 3 hours to investigate. That’s more than 6,700 hours per month.
According to Featurespace’s aggregation of results from using machine learning tech to fight AML, banks can:
- Reduce the number of overall criminal alerts by as much as 12%
- Increase the amount of suspicious activity spotted by more than 130%
- Spot potential criminal activity 1 month earlier than rules-based systems