Cybersecurity and fraud prevention in financial services was a key theme at Sibos, which took place on 23 to 26 in London.
In a session titled ‘Have new business models created a perfect cybersecurity storm?’, CEO of the Information Technology Industry Council Jason Oxman moderated a panel with Microsoft’s Sian John, JP Morgan’s JF Legault, Standard Chartered Bank’s Cherie McQuire and Deloitte’s Rob Wainwright. The executives discussed how the threat landscape has changed in recent years, how cybercrime is becoming more mainstream and how financial organisations are trying to manage their digital transformation while defending against cybercrime.
One of the major contributors to the reduction of fraudulent activities is the effective use of data and artificial intelligence (AI). According to John, phishing techniques are getting more sophisticated. While Hollywood movies frequently depict those creating these attacks as cybercriminals operating in isolation, the reality is quite different – networks of social engineers are manipulating users to click on links through their extensive expertise in social engagement.
AI and machine learning technologies can detect phishing attacks within seconds, blocking them and allowing human experts to investigate the attack more closely. John adds that rather than replacing humans in this field, AI can augment the human experience and provide more effective prevention techniques. Wainwright agreed, closing the session by saying: “we need to use data in a smarter way.”
The SWIFT Institute has also been following this theme, with this year’s Student Challenge asking participants how AI can improve fraud detection and prevention in instant payments.
The winner of the challenge Sam Wincott is a fourth-year student from Cardiff University in the UK who spent his third year working for financial-focused outsourcing company Equinity. He presented a method for using AI to support fraud detection and prevention by augmenting and anonymising data, giving institutions the ability to share data without flouting regulations. This could have countless benefits on the financial services industry, as each can train their machine learning models with more varied and better-quality data from normal and fraudulent financial activities.
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