Preventing procurement fraud in local government with the help of machine learning – Open Access Government

Half of the worlds organisations experienced economic crime between 2016 and 2018, according to PwCs Global Economic Crime and Fraud survey. Government organisations are among these and by no means immune from crime. And yet, the perpetrators are not always remote cyber criminals. The most worrying news is that half of fraud is carried out by agents within the organisation. For many rogue employees, their method is procurement fraud.

Though businesses may often misjudge the cost of fraud, the lack of a definable victim aside from the UK taxpayer means that the figures for fraud which hits public services are unclear. Figures for procurement fraud specifically are even more questionable, though estimates suggest that local councils quashed malicious efforts to steal over 300 million through procurement fraud in 2017/18. The amount undetected by anti-fraud investigators may dwarf that.

The importance of public sector funds not being lost to fraud or wasted has been crucially highlighted in recent weeks by the COVID-19 virus outbreak. These funds, always in high demand, are even more precious in emergency situations like were facing at the moment.

Transparency is a buzzword within the public sector. Citizens are looking for clarity on the value the public sector provides to taxpayers and are demanding more when it comes to services. It is increasingly clear to cash-strapped governments that preventing and detecting fraud is crucial for achieving goals when faced with the alternative of raising taxes which is never popular with the general public. The imperative is to safeguard taxpayers funds and for the public sector to do everything in its power to ensure that these funds are spent on crucial services.

Local government is a particular risk area for procurement fraud. Local governments, including city management,spend a lot of money particularly because many now outsource significant amounts of service provision. They may also lack expertise in contracting and commissioning, and may, therefore, be an easy target for fraudsters. The procurement process is an obvious target.

Procurement fraud can occur at any stage of the procurement lifecycle, which makes it extremely complex to detect and prevent. Analysis suggests that for government organisations, procurement fraud is most likely to occur at the payments processing stage, although vendor selection and bids are also vulnerable stages.

There are a number of ways in which procurement fraud can occur. Some involve collusion between employees and contractors, and others involve external fraudsters taking advantage of a vulnerability in the system. Organisations can also make themselves more vulnerable to fraud by not ensuring that employees follow proper procedures for procurement. One possible problem, for example, is dividing invoices up into smaller chunks to avoid particular thresholds. This is usually done in all innocence as a way to make procurement simpler, but it also leaves the organisation open to abuse because the proper checks are not made.

But if procurement fraud is on the rise, so too is counter-fraud work. Governments around the world have strategies and are monitoring the situation carefully. Many have increased the checks put on procurement processes and have also provided more information to employees and potential contractors about how to spot fraud and potential fraud.

There is growing understanding that rules-based systems are not enough to stop fraud: they may help to detect it after the event, but they are unlikely to prevent it, even in combination with systems to reduce opportunity. Analytics-based systems, however, can both improve detection of fraud, and also start to predict it. They are often based onartificial intelligence(AI), which learns from previous cases, and can then detect patterns that may be associated with fraud, or process breaches that may be a problem.

Detecting anomalies, however, is just one step in the process of preventing fraud. Its only an indicator, and all indicators can do is to indicate. In fraud detection, indicators like anomalies highlight an area for further investigation. Then its over to the fraud, audit and compliance teams to take a look.

Traditional fraud detection has often taken months to complete. Time-consuming audits could detect fraud, but these could begin months after the event, and may only occur once a year. Fraud detection systems based on analytics can spot fraud in a fraction of the time, flagging anomalies to investigation squads in real-time. The actions of those teams can then halt fraud in its tracks, before it takes place, or provide rapid evidence on the perpetrator. Public organisations that put these new technologies in place can rest assured that, with machine learning, fraud detection is not only smart, efficient and speedy, but a frightening prospect for those participating in procurement fraud.

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Preventing procurement fraud in local government with the help of machine learning - Open Access Government