From tackling COVID-19 fraud to helping detect modern slavery: longlist for 2022 Civil Service Data Challenge revealed
Eight groundbreaking ideas to improve the use of data across the UK government have been selected for further development as part of the 2022 Civil Service Data Challenge.
Following a call for ideas from UK civil servants on how the government could make better use of data, over 120 entries were submitted by officials, and the judges have now selected the eight ideas that will go forward for further development.
The challenge – which is a collaboration between NTT DATA UK, the Cabinet Office, Global Government Forum, and the Office for National Statistics – gathered a wide range of ideas for how data could be used to tackle problems and streamline processes.
Last year’s winning team devised a scheme to use artificial intelligence in peatlands restoration work, and this year’s longlist includes using machine learning to trawl Companies House and HMRC datasets to spot behaviours and relationships that may suggest fraudulent activity; plans to make basic security clearances portable across government; and a proposal to provide real-time data to better coordinate government accommodation booking for refugees and asylum seekers.
Read more: A license to think afresh: how the Data Challenge empowered civil service innovators
It also includes a proposal to close existing data gaps by sharing data on self-employed parents with the Child Maintenance Service, and building a dashboard to help detect modern slavery by combining a host of existing datasets such as visa overstayers, high-risk NHS self-discharges against medical advice, and the home areas of offenders convicted of trafficking.
Another idea is to test macroeconomic policies through working with game developers to explore people’s responses to changes in areas such as pricing, inflation and subsidies within online games.
The other suggestions that have made it onto the longlist are a plan to use data to help avert aggressive enforcements against those suffering mental health episodes, and the use of machine learning to improve the flow of correspondence across HMRC so that queries are directed to the right person and can be solved quicker.
‘Great ideas from staff working in many different professions, roles and departments’
Announcing the shortlist, Mike Potter, the government’s chief digital officer, said: “By making better use of data, civil servants can improve public services, cut operating costs, and strengthen the evidence used in policymaking. And as the Civil Service Data Challenge shows, this is not just a task for digital and data professionals: we’ve received great ideas from staff working in many different professions, roles and departments.
“The Data Challenge shows how, working in interdisciplinary, cross-departmental teams, civil servants can rapidly research promising ideas – getting great projects off the ground, developing new skills, and demonstrating the huge potential of the UK government’s data assets. We at the Central Digital and Data Office will be taking a close interest in this year’s Challenge, and I’m excited to see how our eight teams take forward the long-listed ideas.”
Vicki Chauhan, head of public sector at NTT DATA UK said that last year’s inaugural Data Challenge demonstrated how much can be gained from tapping into the inventiveness and enthusiasm of the civil servant community. “There are some promising proposals this year and deciding who will make the final of the Civil Service Data Challenge will be an incredibly difficult task,” she said. “It is fantastic to see such a broad spectrum of ideas in our longlist, from all corners of the civil service, and I’m looking forward to working with the other judges to determine our finalists.”
The full summary of the longlisted ideas is below. Teams of civil servants across different departments, grades and professions will now be formed to develop each idea, with help from NTT Data UK, before pitches are made at a Dragon’s Den style event on Wednesday 7 December 2022. After this, four ideas will be shortlisted to go forward to the final on Thursday 23 March 2023, where a winning team will be crowned.
Read more: Civil Service Data Challenge Final puts innovative AI project on the road to delivery
Civil Service Data Challenge 2022 longlist
Apply machine learning to combat pandemic loan fraud
About 7.5% of the £4.9bn distributed to businesses in pandemic ‘Bounce-Back Loans’ was obtained fraudulently, according to BEIS figures. This idea proposes training machine learning systems with data on known fraudsters and honing them with ‘graph theory’ techniques to enable them to trawl through Companies House and HMRC datasets to spot behaviours and relationships that may suggest fraudulent activity. This would much improve the targeting of audits and investigations, strengthening both enforcement and deterrence work.
Make basic security clearances portable across government
Every government body requires contractors to clear a baseline security check. These can take months, cost an average £115, and are valid for 15 years – but they are not portable, so a new one is required whenever a contractor moves between government organisations. Issuing a standardised, portable credential would save public money by reducing delays, administrative costs and check fees. And a similar system would produce big savings for employers and volunteers in other checking and clearance systems, such as the Disclosure and Barring Service (DBS).
Provide real-time data on government accommodation to aid crisis response
Through the Crown Commercial Service’s buying framework, government departments have purchased more than 4.7 million room-nights for groups including refugees, asylum seekers and those quarantining with COVID-19; an unknown number have been bought outside CCS Agreements. A single spreadsheet is used to track requests and available properties, but there is much duplication and waste in the system. Meanwhile, there is no centralised system recording the accommodation available within government properties. A platform presenting real-time data on available rooms – across the government estate, and among private providers – would both cut administrative costs, and help civil servants to find suitable accommodation much more quickly.
Share data on self-employed parents with the Child Maintenance Service
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.
Build a data dashboard to help detect 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. But data exists 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. These datasets would cover topics such as visa overstayers, high-risk NHS self-discharges against medical advice, and the home areas of offenders convicted of modern slavery or trafficking. 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.
Test macroeconomic policies in the world of multi-player gaming
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, this initiative would see civil servants 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.
Avert aggressive enforcements against those suffering mental health episodes
When individuals have mental health problems, enforcement action by public bodies can worsen their condition without generating any benefits: if depression is preventing someone from filing their company accounts, for example, a fine or prosecution is likely to damage the business while deepening their depression – making it still harder for them to file their accounts. Similar issues exist around services such as Universal Credit, self-employment tax returns and Vehicle Excise Duty. Averting aggressive enforcement action when individuals are suffering a mental health episode, a mental health vulnerability service would help both to minimise the severity of mental health conditions, and to improve the targeting of enforcement at those in a position to respond.
Use machine learning to improve the flow of correspondence across HMRC
HMRC receives 15 million items of customer correspondence annually. Often, the journey of each piece of correspondence to the required team is not a direct one: items may spend weeks or months sitting in the wrong queue before being forwarded on, and be redirected many times before arriving in the right hands. However, we 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, a 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.
About Richard Johnstone
Richard Johnstone is the executive editor of Global Government Forum, where he helps to produce editorial analysis and insight for the title’s audience of public servants around the world. Before joining GGF, he spent nearly five years at UK-based title Civil Service World, latterly as acting editor, and has worked in public policy journalism throughout his career.
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