This page contains a Flash digital edition of a book.
FINANCE AND FRAUD


Mapping the fight against fraud


Fraud costs the UK billions every year, placing a huge strain on government – but with access to national addressing and digital mapping data, the defence against it is growing, as Graham Hughes from Ordnance Survey explains.


An underlying problem has been that many central and local government organisations


have,


over the years, built and maintained front and back office systems which often have multiple address feeds. Local authorities, for example, work hard to keep up with changes in occupancy or address, a task which requires processing across several departments when a person is in receipt of multiple benefits and credits. With perhaps only one department holding the correct information, the system can be prone to errors, time consuming and costly.


Furthermore, the way these systems work inde- pendently can be exploited. One of the fastest-growing


T


he public sector is rich in data. From running public services through to recording and collating public sector information, huge quantities of data are amassed affecting all aspects of government. With so much data being generated it is not surprising that the public purse is such an attractive target for fraudsters, who look to abuse the tax, benefit and grants systems for their own gain.


Fraud and error costs public services in the UK an estimated £21bn a year according to the National Fraud Authority (NFA) 2012 Annual Fraud Indicator. From benefits and tax credits fraud to electoral fraud and council tax avoidance, most areas of central, local government and the NHS are under threat. The same Fraud Indicator in 2012 estimated benefit fraud and error for 2010- 11 at £3.2bn for the Department for Work and Pensions (DWP), while council tax fraud is now thought to cost local authorities £131m a year, an increase of 32% from 2011 figures.


But it is not just loss of revenue caused by fraud that is of concern. Errors in recording and collating public sector information are estimated


46 | public sector executive Sep/Oct 12


at £9.6bn. A recent report by Policy Exchange suggested the public sector could save up to £33bn a year by using its data more effectively. In


“ The ability to link addresses and therefore individuals across public sector datasets offers a valuable new tool which many public sector organisations are now using to reduce fraud and error in the system.”


particular, the report suggests that there is scope to accelerate efforts to reduce local taxation fraud and error.


Difficult to detect


Sharing and understanding the amount and quality of data held by the public sector has traditionally been a challenge due to the number of IT systems which often don’t link to one another. This makes it easier for fraud and error to enter the system and once in the system, more difficult to detect.


areas of fraud is the use of false or bogus ad- dresses across a range of claims. Because the systems are not joined up and often use different identifiers – Na- tional Insurance number (NINOS), Housing Benefit Reference num- ber (HBNR) and Unique Taxpayer Reference (UTRs) to name but a few – to recognise an


individual, these frauds are hard to detect.


Departments such as DWP and HM Revenue & Customs (HMRC) are at the forefront of solving this problem and now run extensive data matching operations to help uncover any fake identities.


Yet, even this work does not prevent fraudsters from continuing to make claims from addresses or properties that no longer exist or are in multiple occupancy with more than one address behind a single front door.


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76