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Using data to improve council tax collections 24 NOVEMBER 2022

Using data to improve council tax collections
4 minute read

Council tax has historically enjoyed a high collection rate – in 2019/20 it was 96.8%[1]. In the two years that followed, the total value collected increased, but the overall collection rate took a dip during the pandemic, before slightly recovering.

Now of course, the UK is facing an unprecedented cost of living crisis, so the question that we at Arum ask ourselves is not about the 95-97% that is collected successfully, but the 3-5% that isn’t. And let’s be clear, these are not small values. In 2020/21, total council tax arrears rose by £847m, and in 2021/22 it rose by a further £521m, bringing total council tax arrears to £4.9bn[2].

How do we go about tackling this, given the huge differences in demographics, geography, population density, and so on, that local authorities have to contend with? One size does not fit all, so we need a dynamic process that tailors treatment to individual circumstances, but of course, to do that, you need to know your customers – and that begins with data.

What data sources are available?

The good news is that there are multiple sources of rich data available in the UK. Let’s take a look at a few:

Credit Reference Agencies (CRAs)

These are probably the most obvious source, but there can be a misconception that what they provide is little more than contact details. Through the predictive analytics that is possible on the millions of records they hold, CRAs can determine:

  • Propensity to pay – you would want to take a different approach to someone who can clearly to pay versus someone who doesn’t have two pennies to rub together
  • Post-default payment behaviour – are they paying other creditors ahead of you? As a priority debt, this could indicate a lack of understanding of the consequences of non-payment, which free debt advice could resolve
  • Debt pursuit by other organisations – is this person in problem debt and if so, can we pair it with other data (such as linked accounts through open banking) to show whether this is a lifestyle running out of control or a life event that has triggered the debt?
  • Seeking new lines of credit – borrowing your way out of debt is rarely the right answer, but it could also anticipate imminent liquidity e.g., if a debt consolidation loan is being sought, then you would want to ensure that priority debts are indeed prioritised
  • Likelihood of future solvency or insolvency – is this a temporary situation that will recover in time or is it likely to come to an abrupt halt through bankruptcy?

Data Enrichment Agencies

These tend to specialise in bespoke services, in which they develop deep expertise. A few examples are:

  • Trace and locate services can reveal hidden relationships between people, properties and businesses, adding layers of validation through credit-linked addresses
  • Hidden incomes through Data-as-a-Service (DaaS) delivered property data assets, sometimes combining over 200 proprietary private and public data sources into a single database

Government data

This includes:

  • Insolvency Service - provides details about bankruptcies, Debt Relief Orders and IVAs
  • Companies House - in addition to data about UK businesses, also holds the Register of Overseas Entities
  • Registry Trust - provides access to the official Register of Judgments, Orders and Fines on behalf of the Ministry of Justice
  • Digital Economy Act - opens up virtually all government data for the purpose of reducing debt

Other data sources

These include the Vulnerability Registration Service and even your own internal historic payment data.

How to use the data

Having all this data is great, but it needs to be used intelligently to drive real value. If it is used in a one-size-fits-all linear treatment path, it’s not being utilised to its full potential, as well as risking detrimental impact on the indebted person (for example, there would be no early identification of vulnerability). This introduces costly failure demand into the process and results in unnecessary delays in getting to the right outcome.

When data, be it geographic, demographic, financial, corrective or circumstantial, is instead combined with proven analytics to segment customers, then they can be automatically routed through differentiated treatment paths. These treatment paths can dynamically change based on Next Best Action principles, underpinned by a feedback loop of previous outcomes by segment, AI and machine learning.

This approach maximises recoveries and minimises costs, while delivering fair debt outcomes for both you and your customer. More importantly, it embeds continuous improvement into the process, and it will refine the segments and treatment paths over time, allowing the system to react with agility to changing political, economic and societal changes.


Case study: Use of HMRC PAYE data to use employer details to secure Attachment of Earnings[3]

Through the Digital Economy Act 2017, a data sharing pilot was set up by 29 local authorities in partnership with the Cabinet Office, using HMRC employer data to tackle council tax arrears. The aim of the pilot was to help councils recover outstanding post-liability order council tax debt and to support vulnerable customers[4].

 As a direct result of the pilot:

  • The councils recovered around £5 million in outstanding debt – this was a collection rate of 20%, against a counterfactual collection rate of 3%
  • 7% of the pilot group (c.1,000 people) were identified as vulnerable and moved onto Council Tax Support
  • Previously unresponsive debtors began to engage with councils for the first time
  • Some in high-earnings brackets repaid their arrears in one payment


Not sure where to start?

While it may seem like a huge undertaking, this does not require massive investment or wholesale transformation programmes.

At Arum, we are seeing:

  • A move towards digital channels and self-service
  • Greater use of cloud – both cloud native and cloud hosted
  • Software-as-a-System (SaaS) delivery models, which are reducing the price point
  • Use of APIs to move away from batch processing to real time
  • New payment methods, such as ApplePay / Google Pay
  • Increased use of Salesforce as it develops in the collections space
  • Use of digital engagement methods, such as QR codes, which are virtually free
  • New challenger and innovations to existing platforms, providing lighter access to the collections technology space

Arum is the UK’s leading independent provider of advisory and professional services within collections and revenue across the public and private sectors. With over 24 years’ experience in over 20 countries, organisations choose Arum to prevent and resolve their problem collections and revenue challenges - whether executing strategic, operational and technical change within their organisations, choosing or implementing collections technology, navigating the inexorable move towards digital engagement, or improving customer treatment to achieve better outcomes.

Please get in touch with me directly if you want to discuss anything relating to this topic.

Steve Coppard
Group Director of Debt Policy & Strategy
Arum & Just

Collection rates for Council Tax and non-domestic rates in England, 2020 to 2021

[2] Collection rates and receipts of council tax and non-domestic rates in England, 2021-22

[3] The Balance Sheet Review Report

[4] Enhancing Council Tax Debt Recovery Using the Digital Economy Act 2017

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