The 2022 finalists
At the Semi-Final, the judges selected four of the eight ideas for further development by our project teams; and at the Final in March 2023, Civil Service Chief Operating Officer Alex Chisholm joined the judges to hear the finalists’ pitches and put questions to the teams. “Today is another powerful reminder that there is a lot of innovation across the Civil Service,” said Chisholm as he announced the winner: Project Heyrick, a plan to use data to identify incidents of modern slavery.
Below we present videos of the four teams’ presentations at the Final. You can also watch all eight teams’ presentations at the Semi-Final on our Longlist page.
THE WINNER: a data dashboard to help identify and tackle modern slavery
Incidents of modern slavery in the UK are typically discovered by public servants working in a wide range of fields, who then make referrals to the police. We have the data to support a much more targeted approach to this crime: a dashboard combining a wide range of datasets would reveal locations and organisations with an elevated risk of modern slavery. Detection and investigation work would then focus both on areas where several risk factors combine, as well as those where gaps in the data may indicate that activity is being hidden from public authorities.
Using online gaming technology to conduct policy experiments in virtual worlds
The fast-growing availability of data has much improved our understanding of the impacts of individual policies and services – but much of this information can’t help us to improve macroeconomic policy, where interventions have complex effects reaching across society. We do, however, have a set of ready-made test beds: thousands of people participate in online games, which could be used to test out economic policies. Following experiments to understand how players’ responses may differ from their behaviour in the real world, we would work with game developers and operators to explore people’s responses to changes in areas such as pricing, inflation and subsidies – providing a unique and valuable set of data to inform macroeconomic policymaking.
Implementing software and artificial intelligence to refine how Digital Mail Service items are categorised
HMRC receives 15 million items of customer correspondence annually. Often, their journey to the required team is not a direct one: items may sit in the wrong queue for some time before being forwarded on and be redirected many times before arriving in the right hands. However, we now have both a vast dataset of scanned items, and data on their ultimate destination – making this problem an ideal candidate for the application of Optical Character Recognition and Machine Learning technologies. Trained using historical data on the final destination of each item of correspondence, an ML algorithm would vastly improve the distribution of mail across the organisation: getting correspondence directly and rapidly to the correct team would save civil servants’ time, speed up casework, and provide a better service to the public.
Connecting datasets across government to improve levels of compliance for child maintenance payments
The Child Maintenance Service is responsible for tracing parents who try to evade their responsibilities and securing maintenance payments. But while its Searchlight system includes data on benefits recipients and the employed, it does not cover the self-employed: the CMS currently maintains a long list of untraced parents, regularly conducting searches for each of them, even while these people complete annual tax returns and report their income to HMRC. Routinely sharing information between HMRC and the CMS would reduce delays, cut administrative costs, bring down the benefits bill, and help prevent parents from evading their duty to contribute to their children’s upbringing.