Use case
P2P Payment Platform Fraud Detection
Investigate a real anonymized data sample from a peer-to-peer (P2P) payment platform, identify fraud patterns, resolve high risk fraud communities, and apply recommendation methods. We will identify new fraud risks that went undetected with non-graph methods, increasing the number of flagged users by 87.5%.Graph Schema: credit cards, devices, and IP addresses. Each user node has an indicator variable for money transfer fraud (named MoneyTransferFraud) that is 1 for known fraud and 0 otherwise. This indicator is determined by a combination of credit card chargeback events and manual review.
Tech stack
PuppyGraph
Apache Iceberg
Docker
Queries in natural language
- Query confirmed financial fraud users
- Show a relational pattern if one user transfer money to another user who shares the same credit card
- Group accounts with transfer records and shared credit cards using Weakly Connected Components (WCC) algorithm
- Find out if there are confirmed fraudulent users within a specific group
- Query users within the specific group: if there are confirmed fraudulent users in the group, or other user in the group may be fraudulent users
Want to try it yourself?
We've open-sourced the sample dataset, graph schema, and graph queries on GitHub, so you can recreate this demo in your own environment.
Visit GitHub Repository
