Protecting Every Pound: A Data-Led Approach to Counter Fraud in Local Government

Counter fraud in local government plays an important role in protecting public money, particularly as demand for essential services increases and budgets and resources remain under pressure. Money lost through fraud, error or financial irregularity is money that cannot be invested in residents, communities and frontline services.

Effective counter-fraud activity is therefore more than a compliance exercise. It is an important part of protecting public money and making limited resources go further.

But fraud in local government can be difficult to identify. Relevant information can sit across council tax, benefits, social care, housing, procurement, payments, grants and supplier systems. When those datasets remain separate, potentially important patterns and connections can be difficult to spot.

Better use of data, intelligent analytics and AI can help local government bring those signals together and give counter-fraud teams a clearer picture of where risk may lie.

Traditional financial controls and rules remain an important first line of defence.

Local government may already use defined criteria to identify suspicious payments, applications, claims or supplier activity. These controls are valuable because they are understandable, repeatable and can be clearly explained.

However, fraud does not always follow a known pattern.

This is where analytics and machine learning can complement existing rules. Instead of looking at an individual transaction or record in isolation, local government teams can analyse information across multiple datasets to identify anomalies, behaviours and combinations of factors that may warrant further investigation.

For example, analysis could help identify unusual patterns across supplier and procurement data, inconsistencies within council tax or benefit records, or connections between records that would be difficult to identify through manual review alone.

Combining established rules with machine learning provides an opportunity to retain clear, defensible controls while introducing an additional way of identifying more complex or changing patterns.

Crucially, a flag or risk score is not proof of fraud. It provides an indication for further review. Counter-fraud professionals still provide the context and investigative expertise needed to understand what the information actually means.

One of the biggest opportunities lies in making better use of information already held across local government.

Procurement teams may hold supplier information. Finance holds payment data. Revenues and benefits teams hold different information again. Relevant external or third-party datasets may provide an additional perspective.

When these sources are examined separately, local government can see only part of the picture.

Microsoft Fabric and Databricks are two examples of data platforms that can help local government integrate data from different sources and use it to identify patterns, anomalies and potential fraud risks. For organisations already investing in these technologies, counter fraud can become another use case for the wider data platform rather than requiring a separate, isolated solution.

Databricks also provides a fraud detection Solution Accelerator demonstrating an approach that combines rules-based fraud detection with machine learning. Applied to the local government context, this shows how established rules for known fraud indicators can work alongside analytical models that identify additional patterns for investigation.

The technology is only part of the equation. Local government still needs appropriate controls around access, security, privacy, data quality and governance, alongside clear purposes for how information will be used.

The objective isn’t to bring together every piece of local government data. It is to connect the right information for a clearly defined counter-fraud purpose.

Some fraud risks may only become visible when local government looks at the relationships between different pieces of information.

The same addresses, bank details, suppliers or other attributes could potentially appear across different records or service areas. Examined separately, those connections may be difficult to identify.

Data and AI can help teams analyse these relationships and identify patterns that warrant further investigation. This provides another way for counter-fraud professionals to explore connections between people, organisations, transactions and other relevant entities.

This does not mean every connection is suspicious. It provides additional information for experienced counter-fraud professionals to investigate and interpret.

Local government counter-fraud teams do not have unlimited investigative capacity.

Technology should therefore help teams prioritise rather than simply create more alerts.

Using analytics to assess patterns and risk can help identify cases that warrant closer attention and direct investigative capacity accordingly. Higher-risk cases can be surfaced for review, while investigators retain responsibility for determining whether further action is appropriate.

This could reduce some of the manual work involved in bringing information together and help specialists spend more time understanding cases rather than searching across systems.

It also creates an opportunity to make counter-fraud activity more intelligence-led, with data supporting decisions about where limited resources should be focused and which cases warrant further investigation.

A data-led approach can help local government strengthen fraud detection by connecting relevant information, identifying patterns and supporting more informed investigations.

With appropriate governance and human oversight, analytics and AI can complement established controls and help counter-fraud professionals make better use of limited resources to protect public money.

The aim is not to replace investigative expertise, but to give teams better information to support their work and help identify risks that might otherwise go unnoticed.

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