The 2016 Annual Fraud Indicator, an industry report produced at the University of Portsmouth, estimated that fraud costs the UK 193 [pounds sterling] billion every year, with 144 billion [pounds sterling] attributed to business fraud. Having more financial services and more personal information online can entice fraudsters but companies can also leverage this information to monitor and reduce risk. Here, Greg Richards, Sales and Marketing Direaor of business intelligence specialist Connexica, looks at how organisations can employ analytics software to manage the risks of fraud.
From paying council tax to paying a leisure centre membership, many tasks that previously required a trip to the local council office can now be completed online. While this is far more convenient for customers, it also gives the organisations a chance to cross-reference this large amount of data to prevent fraud.
According to the National Fraud Authority, fraud and corruption costs local government 2 billion [pounds sterling] a year. At a time when budgets are tight for local authorities, any financial savings have a large impact. Fraud and corruption reduce the amount of resources that are available for legitimate claimants and also reduce the money available for public services.
In response to these figures, Kent County Council's counter fraud team set up the Kent Intelligence Network (KIN). Local authorities involved in the partnership unified a wide range of data before using analytics software to scrutinise the data to find matches and patterns which could potentially indicate fraudulent activity.
By using business analytics software, organisations can search for discrepancies between previously separated data sets such as council tax, benefits and leisure centre records. Council tax records may show that someone claims to live alone but leisure centre records may show multiple people registered at an address. Councils can use analytics software to flag up such discrepancies and investigate further based on quantitative findings.
Streams of data that need to be analysed may come from different types of software, especially when they come from different organisations. To successfully identify any potential...