The battle against fraud is becoming increasingly complex. As technology evolves, fraudsters find new ways to exploit vulnerabilities, creating challenges for businesses and financial institutions. One of the key questions in this battle is: Can we accurately establish an individual’s true identity so we know they are who they say they are?
Identity fraud, synthetic identity creation, and the growing sophistication of techniques like deepfakes highlight the urgent need for robust fraud prevention measures. The costs associated with fraud are staggering, including direct financial losses, investigation expenses, chargebacks, and the harmful impact on customer relationships from false positives and negatives. However, amidst these challenges lies an opportunity. By effectively understanding and managing their fraud risks, businesses can protect their operations and open new doors for growth and innovation.
Artificial intelligence (AI) advancements offer a promising solution in the fight against fraud. Through AI algorithms and the processing power to analyze vast volumes of data in real time, organizations can proactively detect and prevent fraudulent activities. But achieving this goes beyond cutting-edge technology. It requires a comprehensive approach that integrates advanced algorithms with scalable data storage infrastructure to deliver millisecond-level performance.
Graph databases have emerged as an important tool in this fight, providing new capabilities for real-time fraud prevention. By consolidating disparate fraud detection systems and enabling seamless data sharing, graph databases empower businesses to stay ahead of evolving fraud tactics and mitigate risks in real time.
Advantages of Graph-Based Fraud Detection
While graph-based approaches are not new, their integration into modern fraud prevention strategies represents a significant shift. Traditionally, fraud detection relied on disparate data sources stored in relational databases based on tables, rows, and columns, requiring extensive data extraction and visualization to uncover suspicious patterns. However, with graph databases, visualization is easier because the relationships between data points are as important as the data points themselves and are made explicit. For example, two different data points might be the names “Barry” and “Mark.” In a graph database, the relationship could be made further explicit by a pointer from “Barry” to “Mark” labeled “Father.” The entity-relationship graph, central to fraud prevention strategies, is crucial for continuous connected data analysis. This capability ushers in a new era of fraud prevention as enterprises can now use the graph data model to continuously assess and mitigate risk.
Graph technology also offers unique capabilities that can enhance fraud detection beyond traditional methods such as behavior profiling. By using knowledge graphs, organizations can add contextual information about transactions, customers, and other entities involved in the ecosystem and the relationships between them. For example, graph technology can provide insights into whether the customer has previously used the same IP address or device if the same customer is sending in orders from many different email addresses, if there are many transactions from the same household on this device, or if there are connections between different customers sharing the same device.
The ability to navigate these questions allows for a more comprehensive risk assessment because it allows for an understanding of the interconnectedness between data elements. This granular understanding of both transactional and relationship contexts enables organizations to make more informed decisions in real time, resulting in improved fraud detection and prevention.
Requirements for Modern Graph Databases
To effectively prevent fraud in real time, modern graph databases must incorporate three core requirements: advanced AI algorithms, scalable data processing, and real-time performance. From old-guard traditional statistical methods and decision trees to more advanced neural networks and deep learning, the range of AI algorithms continues to expand and drive innovation in fraud prevention.
However, the success of these AI algorithms relies on the ability to swiftly and efficiently process large volumes of data. Modern graph databases must have the scalability to handle terabytes, petabytes, and even exabytes of data seamlessly. As the saying goes, “The more data, the better.” But this requires a robust infrastructure that can process large data sets without sacrificing performance.
Furthermore, real-time processing is essential for effective fraud prevention to ensure a pleasing customer experience, as customers expect a near-instantaneous experience on their mobile devices. Analyzing data in milliseconds allows organizations to instantly detect and respond to fraudulent activities, mitigating potential losses. Real-time performance improves the customer experience and enables organizations to stay ahead of evolving fraud tactics.
A real-world example of graph technology used for real-time fraud detection is PayPal. Over the years, PayPal has significantly improved its fraud detection capabilities, reducing false positives by 30 times and minimizing fraud exposure by almost 98%. Using modern graph databases, PayPal can analyze tens of millions of payment transactions per day in real time, identifying patterns and anomalies indicative of fraudulent activity. This proactive approach allows PayPal to secure users’ accounts and transactions, providing a trusted and secure platform for online payments.
Staying Ahead of Fraudsters with Real-time Graph Technology
Fraudsters are constantly finding new ways to defraud businesses and cause financial harm to customers. To counter these evolving threats, businesses use graph technologies to develop best-in-class fraud solutions. By harnessing the capabilities of graph technology, companies can, as PayPal has demonstrated, proactively stay ahead of fraudsters and protect their assets in real time. Real-time graph databases are essential to help businesses gain a deeper understanding of their customers and transactions to improve the detection of fraudulent activities.
