The nature of fraud has changed dramatically since the dawn of the digital age. According to a recent Office for National Statistics (ONS) survey, one in 10 people in the UK have fallen victim to cybercrime. Companies are no different, with so-called business email compromise schemes netting in billions of dollars for criminal gangs. International money transfer company Xoom, for example, was tricked into sending $30.8m of corporate cash to an overseas account.
This is not a new problem, but it would be churlish to blame companies, as the threat is always evolving. In the 2016 Faces of Fraud Survey, sponsored by SAS, just 34% of surveyed security leaders said that they have high confidence in their organization’s ability to detect and prevent fraud before it results in serious business impact, 56% of whom cited the high level of sophistication and rapid evolution of today’s schemes.
One of the other reasons many of the respondents gave was a lack of awareness among customers and/or partners and employees, cited by 56% and 52% respectively. In order to correct this, both companies and banks – anyone with a serious interest in preventing fraud on a larger scale – are turning to data analytics to identify potential schemes and prevent them before they can take place.
The speed at which money now moves during transactions is an obstacle that cybercriminals have long overcome, and financial institutions have to prevent fraud at the same speed to protect their customers and assets or be held liable for any theft. Machine learning algorithms can help banks find anomalies in the data indicative of fraud in real time, without disturbing the flow of legitimate transactions that must flow seamlessly.