From Names to Networks: Enhancing Sanctions Screening with Graph Analytics

In an era of increasing geopolitical tension and rapidly evolving financial crime, traditional sanctions screening methods are being outpaced by the sophistication of sanctions evasion tactics. Regulatory frameworks are expanding in complexity, sanctions lists continue to grow, and illicit actors are exploiting indirect relationships and opaque ownership structures to bypass restrictions. In response, financial institutions must adopt advanced technologies capable of unveiling these hidden risks. Graph analytics offers a powerful solution by revealing intricate patterns and connections that legacy systems often fail to detect.

What is Graph Analytics?

Graph analytics is a data science technique that examines the relationships between entities by modelling them as graphs. In this model, entities such as individuals, corporate vehicles or accounts are represented as nodes, while their interactions, including financial transactions, ownership links and communications, are mapped as edges. Unlike traditional relational databases, graph analytics focuses on how entities are connected. This makes it ideally suited for uncovering non-obvious relationships across complex networks.

This network-centric approach is especially relevant in financial crime detection. The key to identifying illicit activity often lies not in the behaviour of individual actors, but in the structure and flow of their connections.

How Graph Analytics Enhances Sanctions Screening

Most sanctions screening systems rely heavily on matching names or identifiers against official sanctions lists. However, this method is vulnerable to circumvention by sanctioned entities who use cut-outs, shell companies and complex corporate structures to obscure their involvement. Graph analytics goes further by offering the following advantages:

• Revealing Indirect Associations: Graph models can trace how entities are connected, even when these connections are obscured through intermediaries. For example, a seemingly benign business may appear unconnected to any sanctioned individual but is actually owned by a sanctioned person through multiple layers of companies. Graph analytics uncovers these indirect links and raises red flags.

  
  

• Unmasking Hidden Ownership & Control Structures: Illicit actors often employ nominee shareholders, cross-jurisdictional entities or nested ownership to hide beneficial ownership. Graph-based analysis exposes these structures by mapping how control flows through a network of relationships, even across borders.

  
  

• Prioritising Risk Through Network Metrics: Using measures like centrality (how influential a node is within a network) and degree (how many connections a node has), compliance teams can prioritise investigations. Entities that serve as key hubs in a network may warrant closer scrutiny due to their potential to facilitate or conceal illicit activity.

Implementing Graph Analytics in Financial Institutions

For financial institutions to successfully deploy graph analytics in sanctions detection, they must follow a structured approach.

• Data Integration: Institutions need to aggregate data from multiple internal and external sources. This includes Know Your Customer (KYC) data, transaction monitoring systems, adverse media screening and public registries. The more complete the dataset, the more reliable the graph model.

  
  

• Graph Construction: Once data is integrated, it must be structured as a graph. Nodes are defined, for example as customers, accounts or legal entities, and relationships are mapped as edges such as payments, ownership or shared addresses

  
  

• Applying Advanced Graph Algorithms: Algorithms such as community detection can identify groups of entities acting together. Shortest path analysis can trace how a sanctioned entity is connected to a customer. Anomaly detection can flag unusual connections or structures within the network.

  
  

• Interactive Visualisation & Analyst Interpretation: Visualisation tools enable analysts to intuitively explore the network, investigate paths between entities and identify suspicious clusters or anomalies that require further review.

  
  

• Real-Time Monitoring: To be effective in a fast-moving threat environment, graph models must be dynamically updated. Real-time monitoring enables institutions to respond quickly to changes in the network that might indicate emerging evasion tactics or newly sanctioned entities.

Regulatory Alignment & Model Governance

Graph analytics must be underpinned by robust governance to ensure regulatory defensibility. Institutions should document the rationale behind graph-based models, including data sources, algorithm selection, risk thresholds and false positive handling. Regulatory expectations also demand explainability: institutions must be able to articulate how the tool contributes to compliance, what it detects and what controls are in place to prevent bias or overreach. Internal audit, compliance testing and regulatory reporting frameworks should be updated to reflect this evolving analytical capability.

Technology & Interoperability Considerations

Graph analytics platforms must integrate smoothly with existing compliance infrastructure. Institutions should evaluate open-source and commercial graph databases (such as Neo4j, TigerGraph or Amazon Neptune), and ensure compatibility with case management systems, transaction monitoring platforms and data lakes. API access, data ingestion pipelines and scalability across jurisdictions are critical factors. In particular, financial institutions operating globally must consider how graph analytics can align with local data residency and privacy requirements.

Typology Updates & Threat Simulation

To stay effective, graph analytics must be regularly updated with the latest evasion typologies, such as circular trade routes, vessel reflagging or dual-use goods concealment. Institutions should build or subscribe to typology libraries based on law enforcement and regulatory findings. Additionally, red team exercises can simulate sanctions evasion attempts through synthetic networks, stress-testing the institution’s detection capability and uncovering blind spots.

Key Challenges in Applying Graph Analytics

While graph analytics offers significant advantages in detecting complex financial crimes, its implementation within financial institutions presents several challenges that must be addressed to ensure effectiveness and compliance.

• Data Quality & Availability:

  The efficacy of graph analytics is heavily reliant on the quality and completeness of the underlying data. Inaccurate, outdated, or incomplete data can lead to false positives or missed detections, undermining the reliability of the analytics. Financial institutions often grapple with data silos, inconsistent data formats, and legacy systems that hinder seamless data integration. According to a McKinsey & Company report, financial institutions spend up to 40% of their time on data-related tasks, including gathering, cleaning, and preparation, highlighting the significant resource burden posed by data challenges . Establishing robust data governance frameworks and validation processes is essential to enhance data quality and ensure the integrity of graph analytics outputs.

• Scalability & Performance: Financial institutions process vast volumes of transactions and interactions daily, resulting in complex and expansive networks. Analyzing these large-scale graphs requires substantial computational resources and efficient algorithm design to maintain performance and responsiveness. The scalability of graph analytics solutions is critical to handle the growing data volumes without compromising on speed or accuracy. As noted by Google Cloud, integrating graph data in the cloud can help solve significant challenges in the industry, including scalability and performance issues . Leveraging cloud-based solutions and parallel processing techniques can aid in scaling graph analytics to meet the demands of large financial networks.

• Skills & Expertise Gaps: Implementing and managing graph analytics systems necessitates specialized knowledge in data science, graph theory, and financial crime typologies. Many financial institutions face a shortage of skilled personnel capable of developing and interpreting complex graph models. This skills gap can impede the effective deployment and utilization of graph analytics tools. Investing in training programs to upskill existing staff and recruiting professionals with expertise in graph analytics are crucial steps to bridge this gap. Additionally, partnering with technology vendors and consulting firms can provide access to the necessary expertise and support for successful implementation.

Conclusion: A New Paradigm for Sanctions Risk Management

Graph analytics represents a paradigm shift in sanctions risk detection. By focusing on relationships and behaviours across networks, it enables financial institutions to detect hidden connections, uncover layered ownership and anticipate emerging threats. While challenges remain, especially around data quality and scalability, the potential gains in effectiveness, efficiency and regulatory compliance are considerable.

For institutions committed to robust sanctions compliance and proactive risk management, adopting graph analytics is no longer optional. It is imperative. As the financial crime landscape continues to evolve, those who embrace network-based intelligence will be best placed to protect the integrity of the global financial system.

Could Advanced Data Analytics Enhance Your Ssanctions Programme?

OpusDatum specialises in helping financial institutions to uncover indirect risk associations and enhance risk detection. With expertise in sanctions programme development, risk assessments, and advanced data analytics, we can support your organisation in strengthening its compliance framework.