The DWP will today publish its tough new strategy for tackling benefit fraud. "Reducing fraud in the benefit system" is a new crackdown on benefit fraudsters, using 21st century techniques to beat the criminals.
We will be trialing data matching with credit reference agencies for the first time. This is a major advance and will enable us to check what claimants tell us about their financial situation against what they tell others. We estimate we could save the tax payer £40 million a year by the earlier detection of living together fraud which traditionally has been harder to detect.
The DWP will also explore cutting edge private sector techniques, such as the use of voice stress analysis in telephone claims, to identify suspect cases at the outset.
There will be a change in the way investigators are organised. Through our new set-up we will ensure that investigators are better equipped to identify high risk groups, using analysis of past claims and profiling previous incidents of fraud. This intelligence will enable investigators to detect those who deliberately fail to tell us when their circumstances change, for example.
James Plaskitt said:
"We have already made massive reductions in benefit fraud cutting it to under £1 billion, less than 1 per cent of the total expenditure. In fact we have cut fraud in Income Support and Jobseekers Allowance by nearly two-thirds.
"However, we are not complacent and recognise that there is still much to do. These powers are groundbreaking and will allow us to go after the criminals who steal money and resources from those who need it the most.
"Our investigators have done an incredible job tracking down the fraudsters. New technology and more sophisticated use of data sources will ensure we are even more effective in tackling fraud wherever and whenever it happens. As our new fraud ads say the computers will "work relentlessly, never take a day off and don't even sleep."
Notes for Editors
The strategy paper is available via http://www.dwp.gov.uk/publications/dwp/2005/ssu/reducingfraud.pdf
