Graph analytics helps uncover deep and hidden relationships in complex data. We have partnered with clients in financial services, supply chain, biotechnology and pharmaceuticals to apply graph analytics to targeted marketing, customer 360 and journey analysis, fraud detection, talent development, cybersecurity and more.
Graph analytics can be used to identify potential money-laundering and fraud by analysing complex relationships across customers, accounts, transactions and other entities. We ingest relevant data into a graph model and create a connected network of parties, behaviours and channels.
On top of this graph we apply algorithms to detect unusual patterns, such as tightly connected clusters, circular flows, shared devices or common counterparties. Each customer, account or transaction can be given a dynamic risk score, which feeds into alerts, dashboards and case-management workflows. This approach helps financial institutions and public agencies detect suspicious activity earlier, reduce losses and support regulatory reporting, and it can be applied beyond banking to areas like social security and public safety.
Real-world data is messy. The same person, company or asset often appears differently across systems. Graph analytics supports entity resolution by linking records from multiple sources into a single network of relationships.
By analysing how entities connect to addresses, phone numbers, identifiers, households, companies and other attributes, the graph can suggest which records likely refer to the same real-world entity, even when names or IDs do not match exactly. This helps identify duplicates, aliases and clusters of related parties. Graph-based entity resolution improves data quality for downstream analytics, monitoring and regulatory reporting without requiring a perfect golden source to exist upfront.
Data lineage is the ability to trace where data came from, how it moves and how it is transformed. In complex environments with multiple systems and reports, this is hard to manage using spreadsheets or static diagrams.
With graph analytics, data sources, transformations, tables, jobs and reports are modelled as nodes and relationships. This makes it possible to navigate and visualise end-to-end flows, understand dependencies and answer questions such as “If I change this field, which reports and processes will be affected?”. A graph-based view of lineage supports governance, data quality, impact assessment and regulatory compliance, especially for finance and risk reporting
ESG and sustainability often require tracing the origin and journey of goods across complex, multi-tier supply chains. Graph analytics represents suppliers, manufacturers, logistics providers, certificates and locations as a connected network.
This network can then be analysed to identify where raw materials come from, which suppliers are linked to sensitive regions or practices, and how ESG risks propagate through the chain. For example, a graph can quickly show whether a product contains components linked to deforestation, forced labour or other red-flag issues. This supports responsible sourcing, transparent ESG reporting and better engagement with suppliers on sustainability performance.
Customers interact with organisations through many channels and products. Graph analytics helps bring these interactions together into a connected view, linking customers with their behaviours, interests, relationships and journeys.
By analysing these connections, you can understand how customers influence each other, which products tend to be adopted together, and how journeys differ across segments. This enables more accurate customer-360 views, context-aware recommendations and targeted offers that take relationships and paths into account, not just individual events. The same graph can support marketing, service, risk and product teams with consistent insight.
Internal audit is moving from periodic reviews to more continuous risk monitoring. Graph analytics supports this shift by connecting risks, controls, processes, systems and incidents into a single network.
Analysing this network highlights where risks and controls are concentrated, where issues repeatedly occur, and where there may be gaps or weak links. This allows audit and risk teams to focus on areas of greatest concern, reduce false positives and monitor changes over time. Graph-based views of risk and control help internal audit explain findings visually and prioritise actions with the business.
Supply chains involve suppliers, contracts, plants, warehouses, transports, ports and customers, all interacting in dynamic ways. Graph analytics models this as a network so you can see the true structure rather than just a list of lanes or BOMs.
By analysing this network, you can identify single points of failure, alternative sourcing options, exposure to particular regions or partners, and the impact of disruptions. Graph analytics can also be used with demand and inventory data to optimise stock positioning and route choices. The result is a more resilient and efficient supply chain that is better prepared for shocks and volatility.
Cyber threats form networks: attackers, infrastructure, domains, IPs, malware families, vulnerabilities and targets are all interlinked. Graph analytics turns these elements into a connected threat landscape.
Security teams can use graph views to detect patterns such as shared infrastructure, lateral movement and campaign-level behaviours that may not be obvious in isolated logs. Graph analytics also helps enrich threat intelligence by combining internal alerts with external feeds and historical incidents, revealing hidden relationships and clusters. This supports faster detection, more accurate prioritisation and a clearer view of how threats evolve over time.
Managing workforce skills and roles is increasingly complex. Graph analytics connects employees, roles, skills, projects, trainings and career paths into one view.
This allows HR and business leaders to see where critical skills sit, which teams have overlapping or complementary capabilities, and where gaps are emerging. Graphs can suggest potential career paths and upskilling journeys based on how others have moved through the organisation, and identify communities of practice or informal networks. This supports more targeted development, better succession planning and higher engagement by giving employees clearer, data-driven options.
Digital twins create virtual representations of physical assets or systems. Graph analytics strengthens digital twins by modelling assets, components, sensors, events and maintenance actions as interconnected nodes.
With this structure, it becomes easier to understand how issues in one component propagate to others, which conditions tend to precede failures, and where interventions have the greatest impact. Graph analytics combined with sensor data can support predictive maintenance, root-cause analysis and scenario simulation, helping operations teams reduce downtime and optimise performance.
Large organisations struggle to connect documents, experts, lessons learned and project histories. Graph analytics supports knowledge management by linking topics, documents, people, systems and events into a navigable knowledge graph.
Users can explore connections between past projects, similar issues, subject-matter experts and relevant documents rather than relying solely on keyword search. This accelerates onboarding, problem solving and innovation because teams can quickly find “who knows what” and “what has been tried before” in context. Graph-based knowledge management turns scattered information into a living, navigable asset.
Genomic and biological data are inherently networked: genes, proteins, pathways and phenotypes all interact. Graph analytics provides a natural way to represent and analyse these interactions.
By modelling genomic entities and their relationships as graphs, researchers can discover clusters, pathways and associations that may point to new mechanisms or targets. Graph approaches can complement traditional statistical methods in tasks such as identifying gene modules, exploring drug–target–disease links and understanding complex traits. This helps accelerate insight generation in genomics and related life-science research.
If you would like to explore how these graph analytics use cases could apply to your business, please contact us and we will be happy to discuss potential starting points.
