Earlier today, the Office for National Statistics (ONS) released its latest report. The analysis of fraud and computer misuse shows that this type of crime is more prevalent than many traditional crimes, with data for the year ending September 2017 showing individuals to be 10 times more likely to be a victim of fraud and computer misuse than a victim of theft from the person and 35 times more likely than robbery. Don Duncan, Director at NuData Security commented below.
Don Duncan, Director at NuData Security:
According to these latest figures, fraud accounted for 4.7 million incidents in England and Wales by the end of September 2017, and 3.2 million of those were related to bank and credit account fraud. Unless the UK would like to see these rates climb ever higher, and the damage inflicted on the public continue to grow, institutions, governments, and private companies must take these threats as seriously as other forms of crime.
However, this recorded fraud figure is still astounding, and bad news for consumers who often bear the brunt of many direct costs and pains (especially in account takeover and new account fraud). The increasing volume of global attacks has also been attributed to more fraudsters willing to commit the crime, more data available on the black market, and more financial institutions and merchants that are vulnerable to attacks. It’s incumbent upon companies to secure their customers’ trust by keeping their accounts safe from hackers without hurting their customer experience. They can’t afford to hear their customers say, ‘My account got hacked again.’
To detect out of character and potentially fraudulent transactions before they can create a financial nightmare for consumers and businesses, we must adopt new authentication methods that bad actors can’t deceive. Solutions based on consumer behavior and interactional signals are leading the way to provide more safety for consumers, and less fraud in the marketplace. There are solutions on the market now that can identify machines from humans, then separate good machines from bad, selects known humans from unknown humans, and finally sorts unknown humans demonstrating low-risk signals from unknown humans demonstrating high-risk signals. This process lets organisations fast-track the known and low-risk users for an optimal experience, saving the friction and traditional authentication methods for the highest-risk users.”
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