Optimization of Fraud Detection Model with Hybrid Machine Learning and Graph Database

Abstract

Machine learning and the graph database work well together. By concentrating on the relationships between fraudsters or fraud cases, graph databases can provide an additional layer of security, while machine learning uses statistics and data analytical tools to categorize information and identify patterns within data. In doing so, it can transcend rigid rules and scale human insights into algorithms. When combined with a graph, machine learning alone can increase the accuracy of fraud signals to 90% or higher. On its own, it can reach 70–80%. Graphs also improve machine learning's explainability.