Fraud Detection

The number one reason for creating a knowledge graph is to find the knowledge not visible at first glance. The simplest way to discover new knowledge using a graph database is by matching patterns. Finding new patterns can help you discover fraudulent activities or discover alternative action for guaranteed success.

This blog post deals with solving fraud detection problems with graph machine learning. Learn how to load data, training and plot to find out who did it! It’s elementary, my dear reader.

Check out how Memgraph handles the visualization of complex graph structures found in insurance data that will allow you to comprehend and synthesize large amounts of new information more efficiently.

Find out how to organize data into a graph. Model vehicle insurance claims, incidents, policies and more while preserving the interconnected nature of individual insurance cases.

Are you reluctant to switch from a relational database to a graph databases to explore fraud because you believe you first need to be proficient in Cypher to correctly import the data? Be rest assured - there is a Python-friendly approach available within Memgraph!

If your data is trapped inside tables and you can’t seem to get satisfying answers to questions that would enhance your business, it’s time to switch to graph databases. Here are three main reasons why!

Sometimes choosing graphs solutions isn’t the only step you can take to mitigate risks. In case fraudsters foolishly think they can outsmart an analytics team, they probably haven’t come across their new best friend - fraud detection systems enhanced with machine learning models.

In this tutorial, you will learn how to build a simple graph-powered Python fraud detection web application from scratch.

Learn how graph databases can offer powerful data modeling and analysis capabilities your business can leverage to easily model real-world complex systems and answer challenging questions.