In this blog post, we explain how the latest multi-dimensional analytics tools can augment your existing rules-based defences against fraudulent activity, helping you to apply human-led analysis to outsmart criminals and evolve faster than the threat landscape.
Fraud is estimated to cost the UK economy £190 billion annually, and tackling it continues to be a major challenge in the financial services industry. For UK insurers, investigating suspected fraud consumes hundreds of millions of pounds annually, delays payments to genuine claimants, and drives up premiums for honest customers. From opportunists who exaggerate minor losses on home contents policies to organised gangs committing motor insurance fraud on a massive scale, the Association of British Insurers (ABI) recorded 469,000 fraudulent claims and applications in 2018 – a 3 percent increase on the previous year. In motor insurance alone, there were 55,000 dishonest claims worth a total of £629 million.
Meanwhile, in the banking sector, UK Finance’s “Fraud: the Facts 2019” report suggests that advanced security systems helped stop £1.66 billion of unauthorised fraud, but that £1.2 billion was lost through fraud and scams in 2018. These financial crimes not only leave banks and consumers out of pocket, they also often fund downstream criminal activities including the drugs trade, modern slavery and terrorism. The UK National Crime Agency received more than 450,000 “suspicious activity reports” on money laundering and terrorist financing in 2018, a 10 percent year-on-year rise. Significantly, the organisation believes that less that 20 percent of incidents of fraud are reported to the police.
In addition to detecting and preventing large numbers of purely opportunist crimes, banks and insurers are locked into a costly technological arms-race with increasingly sophisticated career criminals and gangs. Advanced rules-based detection software provides good protection against opportunist frauds, for which patterns of activity may be relatively obvious. However, when professional fraudsters use technology to hide their tracks and evade detection, the industry needs more sophisticated tools that can dig deeper into more complex and diverse data.
How do you currently defend your business and your customers against fraud? How dependent are you on rigid, rules-based approaches, and how quickly can you adapt as fraudsters evolve their attacks? How much time and effort do you spend manually preparing data versus carrying out actual analysis?
Northdoor offers advanced anti-fraud analytics solutions for accelerating and refining the investigation of large volumes of structured and unstructured data. We can help you replace painstaking manual searches with highly automated multi-dimensional analysis, and empower your anti-fraud teams to visualise data based on social, geospatial and temporal connections between persons of interest.
By helping you join the dots between disparate data spread across multiple source systems, our solutions can reveal suspicious patterns of behaviour that are essentially invisible to rules-based systems. The Northdoor solutions also make it easy to present evidence in a way that will be admissible in a court of law.
When there are massive amounts of data to analyse, working with spreadsheets is slow, unwieldy, and makes it hard to spot hidden patterns. If you’re trying to prevent fraud, you want to be able to analyse data almost in real time, and to give human intelligence experts the tools to focus in on the most important events.
Using the Northdoor solutions, analysts can compose powerful queries using a visual drag-and-drop interface – empowering them to pose incisive questions without the need to learn complex computational languages. The net result is a shorter data-to-decision process, potentially slashing the time to complete in-depth investigations, and reducing reporting time from days to hours.
In practical terms, the solutions reduce the time, cost and effort involved in collecting and collating data, and their intuitive drag-and-drop interfaces reduce training requirements. Managed collaboration and workflow support features make it easy for teams of analysts to work together on complex investigations, and continuous data ingest supports near-real-time analysis and decision-making on the latest data.