Researchers found that losses due to fraud cost organizations worldwide more than 80% of the UK’s entire GDP(1) (up to USD 5.127 trillion!). In times of the COVID pandemic, 93% of anti-fraud professionals anticipated an increase in fraud over the next year, and 51% predicted the increase will be significant(2).
Fraudsters continue to exploit new technology to undermine and target businesses and individuals. In the industry, anti-fraud professionals often describe their fight against fraudulent activity as an ‘arms race’(1) that forces businesses and public organizations to constantly find new lines of defense to protect themselves and their stakeholders.
Keeping track of these constantly evolving threats is a particularly onerous problem for business leaders(1). Not only are new threats emerging at an increasing speed, but compliance and risk leaders are under pressure to manage costs and identify, invest, and implement new technologies to strengthen their anti-fraud arsenal and keep up with increasingly sophisticated fraudsters, often operating in organized networks.
In that regard, fraud detection and graph analytics is a match made in heaven. A lot of fraud use cases require identifying suspicious connections whereas graph analytics is designed to analyze complex connections from big data at scale. In this article we will provide a series of examples where graph analytics can be used to fight back against fraud.