Fraud is inevitable when you process billions of payment dollars a year.
How do you find trends and correlations between billions of data records and millions of customer accounts in order to identify money laundering and fraud patterns?
Graph Databases and Machine Learning are to the rescue!
🎯Reserve your seat at the May edition of Innovation · Mindset · Technology Meetup @ Paysafe event to find out more!
📢 SNEAK PEEK
Every year the financial industry loses billions because of fraud while in the meantime fraudsters are coming up with more and more sophisticated patterns.
Financial institutions have to find the balance between fraud protection and negative customer experience. Fraudsters bury their patterns in lots of data, but the traditional technologies are not designed to detect fraud in real-time or to see patterns beyond the individual account.
Analyzing relations with graph databases helps uncover these larger complex patterns and speeds up suspicious behavior identification.
Furthermore, graph databases enable fast and effective real-time link queries and passing context to machine learning models.
The earlier fraud pattern or network is identified, the faster the activity is blocked. As a result, losses and fines are minimized.
On May 16th 🎤 Stanka, Yavor and Dobri will share their experience on how we at Paysafe tackle the non-trivial task of identifying patterns and monitoring transactions on the fly.
The technologies used are Oracle database and Oracle property graph including its fast, built-in, in-memory graph analytics to perform fast graph queries that identify patterns of fraud. However, the approach is general enough and can be applied with other graph databases as well.