Losses due to fraud cost organizations worldwide an estimated 5.1 trillion dollars - more than 80% of the UK’s entire GDP(1). During the COVID-19 pandemic, 93% of anti-fraud professionals anticipated an increase in fraud, with 51% predicting the increase will be significant(2). In this context of increasing fraud, graph analytics are a powerful asset for fraud detection and investigation.
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 in, and implement new technologies to strengthen their anti-fraud arsenal and keep up with increasingly sophisticated fraudsters, who often operate in organized networks.
Fraud detection and graph analytics are 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 graphs can be used to fight back against sophisticated fraud schemes.