Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, today’s sophisticated fraudsters escape detection by forming fraud rings with individuals paid, lured into or unknowingly fronting these activities. To uncover such fraud rings and the people behind them, it is essential to look beyond individual data points to the connections that link them.
Neo4j uncovers difficult-to-detect patterns that far outstrip the power of a relational database. Enterprise organisations use Neo4j to augment their existing fraud detection capabilities to combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering - and all in real time.
Learn more how to battle fraud with the power of graph databases during this webinar. We are pleased to invite you to hear Marius Hartmann from Danish Business Authority talking about how they are combining graph analysis with machine learning to prevent fraud. In context of the COVID-19 compensation scheme controls, he will present use cases currently in production and explain why graph is a good fit for government authorities.