# The Role of Graph Modeling in Fraud Detection Systems–An easy guide to Understand the mechanics behind!

Fraud poses a significant threat to organizations across various industries, making it crucial to employ robust systems that can identify and mitigate fraudulent activities effectively. Graph modeling, with its ability to capture complex relationships and patterns within data, has emerged as a powerful tool in the fight against fraud. This article will explain the fundamental concepts and applications of graph modeling.

**Fraud Detection System Modeling from an Analytical Perspective**

The existing Fraud Detection Systems detect fraud from accumulated data in real-time based on predefined rules. Here, the graph technology can be applied. The most basic approach would be to connect all transactions and actions using the connections (edges) of the graph.

*Read Now* — What is graph data Modeling?

There are two main graph modeling methods often used in combination to detect fraud. The first method is to represent the transaction behaviour and the flow in a single graph. Abnormal transactions and fraudulent activities leave some traces. Modeling the time of the activities and the transaction information from these traces creates a new model that detects transaction amounts, the flow of the transactions, and the relationship between abnormal accounts.

The second model uses heterogeneous network modeling. This model is the method that practically adds and connects data, such as the personal information of the analysis target. The relationship acts as a bridge between the nodes and shares attribute information. (See the image below) Through this heterogeneous graph modeling, we can derive complex relationships that are difficult to find in a single graph.

**4 Values of Fraud Detection System with Graph Modeling**

The following four core values summarize the benefits of implementing graph modeling in FDS.

**Deriving transaction patterns**: Graph modeling can define and derive patterns from transaction flow and the transaction relationship with an abnormal account.**Searching data for fraudulent behaviour**: Modeling both the transactional and fraudulent behaviours into nodes and edges of the graph can help users understand data structure more intuitively leading to efficient actions.**Circular loop pattern**: The circular loop pattern is generated while modeling a heterogeneous graph. When establishing relationships through bridges, the data structure forms a circular loop consisting of nodes and edges. Such a pattern is also known as the Fraud Ring in Fraud Detection Systems. The strength of this Fraud Ring lies in its ability to derive patterns in graph models.**Graph projection**: A relationship established in heterogeneous graph modeling is a relationship that does not exist in raw data. Graph projection is the process of converting relationships from a heterogeneous graph model into a single graph-modeled simple information. This graph projection is beneficial since it enables the formation of new relations during the conversion to a single graph and allows for analysis from different perspectives.

**But how do you get started?**

Now I hope you have a quick overview of how graphs can support the analysis and detection of fraud. But how do you get started? Here are three primary factors to consider before looking into graph modeling.

**The Analysis of Data and domain are the foundations.**

The analysis of the platforms would provide a deep insight into the overall architecture of the target project, such as the research and analysis services conducted during the project. Based on the analysis, issues or limitations within the area of the current domain will be reviewed. This step is crucial in examining if the graph modeling would provide value to the project. The analysis will help to find the difference between existing services and the graph modeling and reveals its worthiness.

**Research & Verify the Modeling Design**

If the project is determined to have positive effects using graph modeling, the next step is carrying out the modeling research and verification. This step will examine the hypotheses of the graph model while looking at experimental designs from various angles.

**Derive value and verify the result via graph model**

Validating the selected graph model and internalizing the values is necessary when applying it to the actual service and performing analysis on similar domains.

With these three fundamentals evaluated, the Fraud Detection System creation process can finally step into the graph modeling phase.

The evolution of fraud detection systems has reached new heights with the power of graph modeling. By leveraging the interconnectedness of data through nodes and edges, organizations can now uncover complex fraud patterns, identify suspicious activities, and mitigate risks more effectively.

AGEDB’s database management system creates a fraud detection system that plays a pivotal role in safeguarding our daily lives. With its enhanced relational and graph database functionalities, AGEDB empowers businesses to store, analyze, and comprehensively visualize data, enabling efficient fraud detection and prevention. To learn more about how fraud detection systems can benefit your organization, visit us today!