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The UK is home to nearly three million "Frankenstein" identities stitched together by fraudsters out of real and made up personal details, according to researchers who warn of a potential multi-billion pound hit to the economy.
LexisNexis Risk Solutions looked at over 72 million consumer profiles and found 2.8 million showing several signs of ’Frankenstein cloning’ to create ’new’ synthetic identities to spoof credit checks and commit high-value fraud against banks and credit providers.In the US, where synthetic fraud is already a major issue, businesses report an average $15,000 loss to each confirmed synthetic fraud case. Translated to the UK, LexisNexis estimates the problem could cost the economy around £4.2 billion by 2027, unless firms start properly screening for the threat.The study found strong evidence of scammers up and down the country using ’synthetic farms’ in rural locations and ’synthetic factories’ in urban areas to build up the credit scores of new synthetic identities on an industrial scale, in readiness for fraud attacks.In one example, rental cottages based on a farm in Chichester appeared to have 439 highly suspect identities ’living’ there over the past seven years, only 22 of which showed any evidence of being real people. The identities were making hundreds of applications for credit, such as short-term and payday loans and some were also linked to a similar farm hundreds of miles away near Dundee, Scotland. Noreen Altaf, identity fraud specialist, LexisNexis, says: "At first, a synthetic ID has little value to a fraudster, as it has no credit history, so they need to play the long game. Scammers nurture each false identity by building what appears to be a real credit profile over time, making the synthetic ID seem like a trustworthy customer - because of this the fraud threat is effectively invisible to firms’ existing fraud defences, until it’s too late."Once a fraudster thinks the synthetic ID has enough plausibility, they’ll aim to max out available credit lines."
By on Thu, 13 Jun 2024 00:01:00 GMT
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