FASHION giant Next has launched AI-driven scanners which see through clothes – and spot stolen goods stashed in bras and pants.
Boffins have perfected technology which spots pinched products inside undies by detecting changes in body heat.
The “Thruvision Passive Terahertz Screening System” has already slashed theft by staff at Next’s UK warehouses and distribution centres – and could soon be rolled out in shops.
Any object concealed in undies – including pilfered items of clothing – can be detected without the need for intimate pat down searches.
Oxford-based Thruvision says its “AI-driven threat detection” technology can also detect hidden weapons, explosives, cash and drugs.
Screening has already cut theft at Next’s UK warehouse and distribution centres across the UK which supply its online business.
But bosses are also considering deploying the high-tech system at their 500 UK stores, which employ 8,000 staff.
Steve Teatum, Head of Group Loss Prevention at Next, told the Retail Crime Uncovered podcast that thieves had been caught wearing layers of stolen clothing.
Mr Teatum said: “We call them ‘Mules’ – we see them loaded up with stuff.
“When we first deployed Thruvision some of the work force at Next had figured out if they were to conceal items within personal areas of their body.
“There was very low risk of that being detected or dealt with.
“What Thruvision allowed us to do was spot anything in those areas – which made it very difficult for a traditional security screening process to identify.
“We knew people were concealing things on the body – we knew they were folding them up and putting them down their bra and their crotch area.
“As gross as that sounds, that was exactly what was happening. Thruvision shone a light on that.”
“We still get people trying to steal in that way but with this technology its very effective in stopping it.”
The cost of staff theft rocketed to £3.3 billion a year – compared with £4.7 billion for shoplifting from stores – and pilfering now accounts for 40 per cent of all retail theft.
