Friday 16 September 2016
Reading Time: 2 minutes
In recent years, fraud has become a recurring problem for local authorities in the UK. Back in 2013, it was found that as many as one fifth of London council tenancies showed ‘indications of fraud’, and the Annual Fraud Indicator (AFI) concluded that a figure of £52 billion had been taken from the economy due to fraudulent activity.
While it is relatively easy to identify the high incidence rates, it has been significantly more challenging to identify the type of fraud committed. Every year the UK government estimates the percentages of services and benefits that are taken fraudulently, but these are only approximations. In reality, the figures may be much higher.
There is, however, a clear reason why so many instances go undetected. Due to the lack of resources, there is simply not the infrastructure to provide extensive analysis that would uncover such occurrences. It is this analysis that is necessary to discover the inconsistencies, with housing benefit fraud, for example, often discovered by cross-referencing service bills, such as banking or utilities, with housing records. When inconsistencies are present, it can often be the first indicator of fraudulent cases.
While accumulating this data poses no difficulty, it is cross-referencing the separate data sources that poses a problem. Traditionally, local authorities store the collected information in rudimentary databases, with some still opting to use Excel spreadsheets. This makes the process of extracting key data very time consuming, which is further aggravated by the high volumes of data being processed.
By opting to incorporate traditional solutions to make sense of the data, the reliance on trained analysts or the council having to spend a large amount of money training certain members of staff may result in very high operating costs. As local councils must operate cost effectively, neither option is viable for regular analysis.
To combat this, there needs to be a technological shift to democratising business intelligence. This requires an understandable means of interfacing with the masses of data, allowing more members of staff within an organisation to gain actionable insight from self-service business intelligence, negating the long waiting times and overreliance on IT departments usually associated with traditional solutions.
One example is search-powered analytics, where data is available via natural language search rather than complex programming languages. By utilising this technology, anyone familiar with an internet search engine can navigate key data and can unify previously siloed datasets into a single interface, providing key decision makers with a single version of the truth.
This approach allows staff from all areas on an organisation to contribute to a data-driven culture, where decision making is based on sound data confidence. Where local authorities are concerned, this kind of insight could prove vital to recognising trends and making valid adjustments to services to reduce instances of fraud while making substantial cost savings.