Graph-Based Approach to Cybersecurity in Financial Services: A Case Study of Capitec
With the increasing sophistication of cyber threats, financial institutions are turning to innovative solutions to fortify their cybersecurity infrastructure. One such approach gaining traction is the use of graphs, a method typically associated with modeling social network interactions.
There is a growing body of evidence suggesting that graphs are particularly effective in cybersecurity within financial services. The reason behind this lies in the nature of data collected across cyber systems. This data often reveals interactions that are best modeled as graphs rather than traditional relational databases. Graph-based methods provide a comprehensive view of emerging threats by identifying networks of malicious actors attempting to breach defenses, complementing the monitoring of individual behaviors.
The Capitec Experience: Graph-Based Fraud Detection in Action
Capitec, South Africa’s fastest-growing retail bank and largest digital bank, presents a real-world example of a financial institution successfully implementing a graph-based approach to cybersecurity. Serving over 25 million clients as of 2025, Capitec is committed to making banking accessible to all, despite the rising challenge of fraud in the country.
The bank has been using graph technology for the past two years to leverage relationship-based insights to combat cybercrime. Capitec transforms transaction data into connected fraud graphs, generates graph-based features, and feeds these features into a specialized internal fraud scoring pipeline. The result is a graph-based fraud detection system capable of identifying high-degree connections and hidden fraud patterns. This system processes over 3.5 million records per day, demonstrating cybersecurity on a large scale.
Relationships Matter in the Fraud Problem Space
The increasing sophistication and complexity of banking fraud often involve networks of accounts, devices, and identities. Traditional, rule-based systems often struggle to detect these coordinated schemes. Instead, Capitec employs knowledge graphs to map relationships among entities like customers, accounts, transactions, and devices, enabling the detection of intricate, networked fraud patterns.
The bank’s interest in a graph-based approach arose from two main challenges – the highly networked nature of fraud and the criticality of speed in detecting fraudulent activities. The graph-based approach offers the ability to connect data points and visualize relationships in near real-time, enabling analysts to see the bigger picture and respond swiftly to suspicious transactions.
Proof in the Cyber Pudding
Capitec’s knowledge graph has evolved over two years to integrate multiple data sources, including transaction histories, customer profiles, device IDs, and public watchlists. By linking these entities, investigators can identify suspicious patterns that traditional systems would miss. The graph-based system also allows analysts to curate alerts and workflows effectively, reducing false positives, and improving investigative focus.
This approach means that analysts can detect up to 50% more suspicious activity compared to the legacy system, while investigation time fell by 30–40%. The key insight here is the importance of tool curation and context awareness. Overloading the system with too many detection parameters or automated scripts can introduce confusion and lead to misfires.
Time to See if This Could Work for You?
By connecting disparate data points, visualising networks, and curating both tools and context, organizations like Capitec, and other financial services firms and banks concerned about the rise in fraud, can mount an effective, proactive defence against even the most sophisticated schemes.
For banking institutions facing increasingly complex threats, knowledge graphs are an essential tool for protecting revenue and customer trust. It might be time to consider whether a graph-based approach could work for your organization?
Marko Budiselic is Co-Founder & CTO of Memgraph
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