The banking and finance industry faces significant challenges in fraud detection due to the increasing sophistication and volume of fraudulent activities, particularly in credit card transactions. Traditional rule-based systems struggle with real-time detection and adaptability to evolving fraud tactics. Advanced solutions like graph analytics and real-time stream processing offer more effective fraud detection capabilities by identifying complex patterns and relationships in transaction data. PuppyGraph, a graph query engine, and DeltaStream, a stream processing engine powered by Apache Flink, provide an integrated approach to enhancing fraud detection efficiency and accuracy. PuppyGraph simplifies the complexities of graph database implementation by offering zero-ETL integration with existing data infrastructures, while DeltaStream enables real-time data analysis to quickly identify and respond to suspicious activities. Together, they empower businesses to build dynamic fraud detection systems capable of staying ahead of modern fraudsters by leveraging real-time data processing and graph analytics.