Combat Fraud with Graph
Estimated reading time: 5 minutes
Fraud is one of the most significant issues facing businesses today. While companies have always faced fraud, detecting fraudulent activity has become even more challenging due to increased online transactions. Globally, fraud results in more than $3.7 trillion in annual losses (Murphy, 2022). Fraud comes in numerous forms, including but not limited to money laundering, identity theft, account takeover, and payment fraud. Due to the variety of ways companies can face fraud, they must have a system to protect themselves and their customers.
One technology that is increasingly being used to detect and prevent fraud is graph databases. This blog post will explain graph databases and how they can help with fraud detection.
What are Graph Databases?
A graph is a collection of nodes (points) and edges (lines) where the edges describe the relationship between the nodes. Graphs can be explored through graph theory, analytics, and database models. This database form is considered the next step for data and analytics to get the most out of their delivery. Graph databases give a way to organize and present data for use cases previously considered difficult or complicated to address appropriately.
A graph database stores the data and its natural relationships as a graph of nodes and edges instead of disconnected rows and columns in a table that you would see in a traditional relational database.
Graph Database vs. Fraud Detection
Graph databases are ideal for fraud detection because they can quickly and efficiently identify patterns and connections between seemingly unrelated entities. Here are a few ways in which graph databases can help with fraud detection:
Relationship Analysis: Fraudulent activity often involves multiple entities, such as a fraudster, a victim, and a middleman. Graph databases can identify behavior patterns indicative of fraud by analyzing the relationships between these entities. For example, if a fraudster uses multiple email addresses to create fake accounts, a graph database can identify them and link them to the same person.
Real-time Detection: Graph databases can process large amounts of data in real time, making them ideal for detecting fraud as it happens. By continuously analyzing data streams, graph databases can detect behavior patterns indicative of fraud and trigger alerts or block transactions.
Machine Learning: Graph databases can be integrated with machine learning algorithms to improve fraud detection. By training machine learning models on historical data, graph databases can identify behavior indicative of fraud and use those patterns to predict future fraud.
Learn more about Machine Learning with ArangoDB and ArangoGraphML here.
Centralized Data Management: Graph databases can provide a single source of truth for fraud detection data. Companies can easily track and analyze fraud patterns across different systems and departments by collecting and storing all fraud detection data in a graph database.
Entity Resolution for Fraud Detection
Entity Resolution is a critical tool in combating fraud. Entity resolution finds duplicates of entities across multiple systems on-prem and in the cloud. This allows admins to have a clear view of data and to sort through different data types such as date, contact, email, address, email, device, or any additional unique identifier.
When applied to fraud detection, entity resolution allows admins to find duplicates of fraudsters within their system by cross-referencing these unique identifiers. This will enable fraudsters running different scams through similar networks to be flagged and taken down.
ArangoDB as a Graph Database
ArangoDB goes beyond graph by being a graph store that natively incorporates capabilities from other data models, including key-value, document, search, and more. The graph capabilities of ArangoDB are similar to a property graph database but add more flexibility in data modeling as vertices and edges are both full JSON documents.
Due to this natively integrated support, users can take the result of a JOIN operation, geospatial query, text search, or any other access pattern as a starting point for further graph analysis and vice versa – all in one query, if needed.
ArangoDB is the underlying database for ArangoGraph Insights Platform, a cloud-based graph data, and analytics platform.
Fraud Detection with ArangoDB
Today’s criminals are developing new techniques to hide their activities by forming fraud networks with stolen or synthetic identities. Attacks are often launched from multiple vectors and can only be discovered by connecting diverse data sources to uncover difficult-to-detect patterns. Native graph technology is perfect for solving this challenge.
A graph database is a suitable solution as it decreases the time needed to process fraud detection queries against the database and delivers simple data visualizations to analysts. It also removes false positives as real customers wait for the money (customer satisfaction and lost revenue). Fraud detection is a great use case for a graph database, as relational databases are too slow and complex to query in real-time.
Try out our Fraud Detection guide on ArangoGraph Insights Platform → https://cloud.arangodb.com/home?utm_campaign=2023%20Fraud%20Campaigns&utm_source=fraud%20detection%20blog
Fraud detection is a critical aspect of modern business, and graph databases can help companies detect and prevent fraud by identifying patterns and connections in complex and interconnected data. By leveraging the power of graph databases, companies can improve their fraud detection systems and better protect themselves and their customers.
Read “Identifying Fraud at Scale with ArangoDB”→ https://www.arangodb.com/resources/white-paper/fraud-detection/?utm_campaign=2023%20Fraud%20Campaigns&utm_source=fraud%20detection%20blog
Did you miss our “Fraud Detection with ArangoDB” webinar? Watch it on demand today! → https://hopin.com/events/fraud-detection-with-arangodb?utm_source=Blog%20Post&utm_campaign=Fraud%20Detection%20Campaign
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