Half Of UK Organisations Say They Have Been The Victim Of Fraud And/Or Economic Crime

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It’s been reported that, according to PwC’s ninth biennial Global Economic Crime & Fraud Survey, half of UK organisations say they have been the victim of fraud and/or economic crime in the last two years. More than half (51%) of the most disruptive crimes resulted in losses over £72,000, compared to 37% globally. Nearly a quarter of UK victims (24%) lost more than £720,000 as a result. Robert Capps, vice president of business development at fraud prevention firm, NuData Security Inc., a Mastercard Company commented below.

Robert Capps, Vice President of Business Development at NuData Security Inc.:

“The magnitude of these losses can’t help but have a dampening effect on the UK economy and on those businesses who are experiencing losses of over £72,000. It’s also bad news for customers, who often bear the brunt of many direct costs (especially in account takeover and identity theft).

“With 3.2 million fraud incidents last year, fraud is becoming a tempting promise of high reward and low prosecution rates. Emboldened cybercriminals are becoming more technology savvy and are increasingly posing as banks or suppliers and then duping customers into revealing their personal details. These scams have also proved effective in targeting commercial organisations, as senior executives have been tricked into revealing sensitive information which enables access to a company network. The increasing volume of attacks globally has also been attributed to more data available on the black market and more financial institutions and merchants vulnerable to attacks.

“To detect out-of-character and potentially fraudulent transactions before they can create a financial nightmare for consumers – and for companies – we must adopt new authentication methods that hackers can’t deceive. Solutions based on passive biometrics and interactional signals are leading the way to provide more safety for consumers and less fraud in the marketplace. These solutions 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. These layers validate the user through information that hackers can’t replicate, securing the good user’s transaction at every step.”

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