Graph databases play a key role in fraud detection within intricate, complex networks, helping security teams keep pace with modern fraud techniques that are becoming increasingly more sophisticated. Graph databases can identify patterns and relationships in big data, reducing the level of complexity so that detection algorithms can effectively discover fraud attempts within a network.
Below, we will analyze how graph databases are revolutionizing fraud detection algorithms, highlighting key relationships that traditional data models cannot. Using real-world examples, this article will provide a comprehensive overview of how interconnected data can expose fraudulent activity such as money laundering.
What Are Graph Databases?
Graph databases store and examine relationships in a network, working at very fast speeds, making them valuable for a range of use cases, including fraud detection, recommendation engines, network mapping, and social network algorithms.
These databases use nodes for data storage, while edges store relationships between data. Each edge has an end node, start node, type, and direction, and each can identify parent-child relationships, ownership, actions, and more.
The individual graphs within a graph database can be followed along certain edge types or over the graph in its entirety for complete visibility. Then, connections between the links and relationships between nodes can be processed extremely quickly because they are persistent in the database and not calculated for each query.
Graph Databases vs. Relational Databases
The key difference between a graph database and a relational database is that graph databases store relationships between data as data entities. Relational databases, on the other hand, focus on identifying relationships between columns of data tables, not between data points, and they store data in tables.
In a graph database, new nodes can be added easily and complex queries can be processed quickly, making them the ideal option for projects that use real-time data. Meanwhile, relational databases are suitable for more simple relationship structures.
Graph Database Fraud Detection
Fraudsters, with the help of advances in technology, have devised more sophisticated techniques and tactics to bypass traditional detection systems. Fortunately, organizations such as banks and financial institutions have been able to use a graph database approach to stop criminals in their tracks.
Graph databases can identify suspicious behavior and unusual activity that was previously impossible to detect using relational database management systems (RDBMS), which aren’t as good at processing real-time data. Now, thanks to graph databases, fraudulent activity can be flagged and prevented much earlier and more effectively.
Two types of fraud that graph databases can help to prevent are money laundering and credit card fraud.
Traditionally, anti-money laundering (AML) systems are built on RDBMS, which stores data in rows and columns across tables, akin to a spreadsheet that uses tabs. This makes it very difficult to identify the complex relationships that modern money laundering techniques utilize to disguise money trails.
A common money laundering method is to divide a large sum of dirty money and transfer it, via multiple (often several dozen) individual transactions, to a number of different bank accounts and identities, creating a complex web. The money is then divided once more, into even smaller amounts, and sent to intermediary accounts. These accounts then aggregate the money into a pool of funds.
This process is repeated multiple times, each time adding another layer of complexity that makes it much more difficult to locate the source of the money.
Graph databases are capable of storing and mapping such activity, however, making it easier to link payments together and reveal how money is moving from its original source to individual pooling accounts. Graph databases make it possible to identify money trails regardless of the level of complexity – even if hundreds of transactions have been made.
A traditional SQL relational database is not capable of re-creating such a complex structure due to the large number of inner joins. Furthermore, the cost to scale such analysis in real-time would not be viable with a relational database approach.
Credit Card Fraud
Credit card fraud, or financial transaction card fraud, occurs when a criminal uses a stolen or fake card, or when they apply for a credit card using a fake identity. Fraudsters may do the latter by obtaining someone’s personally identifiable information or by manipulating identifying data (such as social security numbers, email addresses, phone numbers, and home addresses) to create fake identities. At the beginning, they use their illegally obtained credit card(s) normally, making on-time payments and gradually increasing their card limits.
Once their credit limit reaches the desired level, a fraudster will “max out” the card and not repay it. Linking a fraudulently used card to a criminal’s actual identity is extremely difficult, resulting in uncollectible debts that are often written off as losses. This type of fraud can cost banks billions of dollars each year.
Fortunately, graph databases make it possible to detect bad actors that are involved with this type of fraud. The detection process involves a different technique than money laundering detection does. With credit card fraud, instead of identifying money trails, graph databases highlight shared identifiers, such as the addresses or social security numbers associated with cards.
This technique is called link analysis, and it works by analyzing relationships between the nodes and edges within a network. Recognized as an extremely powerful tool in fraud detection, this graph database analysis makes it possible to piece together relationships between connected data elements, like the manipulated data used to create fake identities.
Graph databases can prove crucial in several use cases, including playing a pivotal role in fraud detection. The ability to analyze data quickly, in order to identify and then store relationships between data, makes it possible to spot unusual activity even across the most complex of networks.
Fraudulent activity such as money laundering and credit card fraud has become increasingly more sophisticated, moving beyond the capabilities of traditional fraud detection models that use relational database management systems. As a result, banks and financial institutions have turned to a graph database approach to pinpoint fraudulent activity at its source.