Data analytics and the COVID-19 recovery
In response to the COVID-19 pandemic, the UK government has rolled out a range of grants, loans and guarantees to limit the fiscal and economic impact of the crisis in the longer term.
One initiative was the ‘Business-led innovation in response to global disruption‘ – a funding competition hosted by Innovate UK.
Organisations could apply for a share of up to £20 million to help respond to new and urgent needs in the UK and global communities during the COVID-19 pandemic.
Unprecedented application volume
The competition received almost 8,500 applications in just 16 days. Innovate UK typically expects to receive between 9,000 and 10,000 applications across all competitions in a single year.
The Innovate UK team were tasked with: identifying duplication and potential fraud; matching submissions to their pool of assessors; and arriving at a robust decision within a short timeframe.
Machine Learning & Text Analytics
Deployed with Innovate UK since 2018, Analytics Engines’ data solution ‘Cobalt Grant Manager’ utilises automated Machine Learning, Text Analytics and Natural Language Processing technologies to:
- perform similarity checks for multiple submissions of the same application
- match against resubmissions of applications that have previously been rejected
- compare submissions against previously funded submissions.
Cobalt Grant Manager ensures that the information presented to the end-user is accurate, comprehensive and most importantly, transparent.
According to James Danek, Applications and Assessment Team Leader at Innovate UK: “Identifying and reducing the risk of fraud is a primary concern for Innovate UK. Using Cobalt Grant Manager, we screened for potential fraud and duplicate applications in a very efficient and comprehensive way. Grant Manager gave us the assurance and confidence we needed to quickly move to the next stage of the process.”
Find out more
To discover more about Cobalt Grant Manager and how it has transformed operations at Innovate UK, you can view our case study here.
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