Our data analyst Ben completed some work a couple of weeks ago for one of Veritau’s council clients.
The project involved finding fraud and error in the micro grants scheme, which makes grants of up to £1,000 available to businesses who are not eligible for other Covid-19 business support. Micro grants allow these businesses to adapt to current circumstances, eg installing remote working equipment.
The project had three goals:
- Find and report duplicates in the micro grant applications
- When first launched, there were some technical issues with the application website. This caused some applicants to accidentally submit multiple forms.
- Match micro grants to main grant applications
- Businesses can only receive one grant, so we needed to flag where someone had applied for both. Some of these cases will be due to lack of understanding, but some may be attempted fraud.
- Match micro grants to business rates data
- If a person of business is eligible for the main grant, they can’t receive a micro grant. So we needed to flag anyone who had applied for the wrong scheme. Again, this could be fraud or just a genuine mistake.
The main challenges:
- Data quality
- There was no data validation in the front end of either application process (due to the short implementation time) and almost every field was a ‘free text’ field. This meant the data was rather poor quality.
- Inconsistent formats
- For the same reason as above, no two addresses, DOBs or names had the same format within the data. This made analysis work difficult and comparing datasets extremely challenging.
- When formats are not the same, eg a postcode is contained in the address field of one dataset but has its own field in the other, this makes it difficult to conduct analysis like looking for duplicates.
- We had two working days to turn around the project, and only one staff member to do it.
- Found 289 potential duplicate applications
- There were only 1,070 applications to start with, so this was a surprising result. One applicant had submitted 12 applications.
- Found 54 applicants who had already applied for a main grant
- There’s no way of telling which were mistakes and which may have been attempts at fraud. But it’s a result that would’ve been very difficult and time-consuming to achieve manually without data analytics tools.
- Found 44 applications who were in the business rates system
- And therefore could obtain a grant on the main business grant scheme.
- Potential savings of £1,000 per finding for our client
- Of course many of these issues may have been spotted in other ways, but using data analytics is an effective method of getting this information quickly.
- Councils are under immense pressure and being able to find potential fraud and error in the system saves them valuable internal resources.
A second round of analysis was carried out once more applications had been received. There were a further 150 potential duplicates, 68 potential instances where the person was eligible for another grant, and 13 potential instances of people applying when they had already applied to the main grant scheme.
Final result: one pleased client but one tired data analyst!
- What's it like being a data analyst in the world of counter fraud?
- Auditing with data analytics tool IDEA
- 7 types of fraud on the rise due to Covid-19
Want to know more? Contact our corporate fraud team