Software that does away with the tedious and time-consuming task of typing rows and rows of transactions accurately, allowing data to be quickly scanned and then converted into a spreadsheet, is proving invaluable in managing financial and fraud investigations.
When Cleveland Police received a complaint from The Department of Education and Skills (DfES) concerning the involvement of a company in the Individual Learning Account (ILA) scheme, it started an investigation that uncovered a multi-million pound fraud.
The allegation was that the company’s method of doing ILA business defrauded both the Government and individual students.
The accused was the owner of the National Distance Learning College (NDLC) in Middlesborough. At the time, the company was offering home study courses in the field of computing and business studies. It was then the UK’s largest learning provider of its type.
In the space of three years, the company obtained approximately £10 million in total from around 80,000 students. Many of these students were seeking nationally-recognised qualifications, such as BTEC [Business and Technology Education Council] or City and Guilds, which were featured in the company’s advertising.
Only 18 of the would-be graduates ended up with a genuine, nationally-recognised qualification. Many of the rest were sent certificates issued by another of the accused’s companies, The Association of Professional Development.
The ILA scheme was set up by the Government in 2001 in an effort to encourage people to take up adult learning.The ILA scheme allowed learning providers such as NDLC to claim a Government grant in respect of each student who enrolled on one of their courses. During the life of the scheme, the accused’s companies obtained some £5.8 million in ILA grants, in addition to the £10 million obtained from students.
Much of the proceeds of this £16 million fraud were subsequently found to have been spent on racehorses, gambling, foreign travel, home improvements and other business ventures.
The Cleveland Police investigation team discovered that NDLC and other companies, including Assert Training Ltd and the Association of Professional Development, were part of Thanx Group, which was owned by Michael Smallman.
A critical part of the evidence was the movement of monies between various bank accounts. In total, Smallman had access to over 50 accounts.The police subsequently obtained some 10,000 pages of bank statements relating to these accounts.
To assist in proving the fraud, investigators had to schedule all bank accounts.
To save time, the team used the Altia FI Toolkit, estimated to be at least eight times faster than manual entry, to turn bank statements into spreadsheets. These were subsequently used to identify links between accounts and to prepare reports and charts for the prosecution.
“With close to 10,000 pages of bank statements to investigate, the FI Toolkit helped us save substantial amounts of time and resources,” explained Detective Inspector Dave Turnbull of the force’s economic crime unit.
Many of the completed schedules, reports, charts and scanned bank statements were copied to CD ROM. This saved time when producing copies for prosecution and defence teams and the court prior to and during the case.
Another major benefit of the FI Toolkit was that even in such a long and complex trial, the financial evidence was produced in a consistent format that the jury was able to easily understand.
Smallman was eventually sentenced to a total of seven years imprisonment while his wife was jailed for 15 months for money laundering offences.
FI Toolkit software does away with tedious and time-consuming typing of rows and rows of bank statement transactions accurately. Instead, the information is scanned quickly and then converted into a Microsoft Excel® spreadsheet – at least 80 pages in the time it used to take to enter ten.
The F1 Toolkit’s ‘wizard’ manipulates and error-checks the data and the final result is a consistent, accurate set of transactions, regardless of the source document’s format, making it easier to compare, research and categorise the data.