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Fraudsters constantly evolve, using synthetic identities, mule accounts and webs of transactions to evade detection. Regulations require banks to identify suspicious patterns and report them promptly, yet rules-based systems often miss sophisticated schemes.

• Rocketgraph models the network of customers, accounts and transactions to compute centrality and community metrics in real time, uncovering hidden patterns and suspicious chains.
• Rules-based systems produce false positives and miss organised rings; manual investigation is slow and expensive.
• Without advanced analytics, fraud persists, leading to financial losses, fines and reputational damage.
• Earlier detection reduces fraud losses and false positives, saving millions and protecting customer trust.

Assessing credit risk requires analysing a borrower’s behaviour, relationships and exposure to macro factors. Standard models treat applicants individually and ignore connections among borrowers, guarantors and employers.

• Rocketgraph integrates borrower data, employment histories and market feeds into a risk graph, computing network-based risk scores and revealing hidden concentration points.
• Traditional models fail to capture cascading defaults spread through relationship networks.
• Ignoring network effects can lead to systemic failures when a single collapse cascades through guarantor chains.
• Network-aware risk models enable accurate pricing and early identification of emerging risks, reducing defaults and capital shortfalls.

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A 360-degree view requires combining data from savings, loans, investments, interactions and external sources. Graph models represent a customer and all their relationships so institutions understand their full financial picture.

• Rocketgraph unifies disparate data into a customer graph and allows marketers to query it in natural language to find life events and cross-sell opportunities.
• Without a unified view, marketing is generic and misses contextual opportunities.
• Institutions risk losing customers to competitors and waste budget on irrelevant offers.
• Personalised offers increase conversion, retention and revenue per customer, yielding double-digit uplift.

Banks must trace the provenance of funds and maintain audit trails of complex transactions to satisfy regulations like KYC and FATCA. Traditional systems struggle to stitch together relationships across multiple sources.

• Rocketgraph creates a traceability graph linking customers, accounts, transactions and documents, enabling instant queries over lineage and beneficial ownership.
• Manual compliance reporting is error-prone and time-consuming.
• Non-compliance results in hefty fines, sanctions and reputational damage.
• Automated graph-based reporting reduces audit times, improves accuracy and mitigates compliance risk.

Traders ingest live market feeds and react to price movements across correlated instruments. Understanding influence networks among equities, derivatives and news is key to strategy.

• Rocketgraph processes streaming price and news data to build dynamic correlation graphs, allowing algorithms to detect emerging patterns and arbitrage opportunities.
• Static models miss transient correlations in rapidly changing markets.
• Missed signals lead to lost alpha and increased trading risk.
• Real-time graph analytics can enhance strategy performance and increase trading returns.

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Exchanges monitor order books, trades and communications to detect manipulation and insider trading. Suspicious patterns often involve collusion among traders and brokers.

• Rocketgraph links orders, trades, accounts and communications to uncover collusive rings and unusual behaviours.
• Isolated data streams make it hard to detect coordinated manipulation.
• Undetected manipulation undermines market integrity and invites regulatory action.
• Graph surveillance reduces investigation times and preserves market fairness.

Insurers must detect fraudulent claims involving staged accidents, exaggerated damages and collusion among claimants, providers and attorneys.

• Rocketgraph models relationships among policyholders, vehicles, providers and incidents to spot suspicious patterns like shared addresses or repeated participants.
• Rule-based systems are easily circumvented by organised fraud rings.
• Fraud drives up premiums and erodes profitability.
• Advanced detection reduces fraudulent payouts and protects customer trust.

Asset managers need to balance portfolios across multiple asset classes and issuers. Understanding how entities are connected through ownership, supply chains or market factors helps avoid concentration risk.

• Rocketgraph maps relationships among companies, sectors and financial instruments and computes diversification metrics.
• Traditional risk models treat exposures independently and ignore cross-company ties.
• Hidden correlations can lead to losses during market stress.
• Graph-driven diversification improves risk-adjusted returns and resilience.

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Advisors aim to provide personalized investment advice based on clients’ life events, holdings and goals. They need to surface relationships between clients, their networks and market opportunities.

• Rocketgraph builds knowledge graphs connecting clients’ financial data, social relationships and market insights, enabling personalized recommendations.
• Fragmented data sources make it difficult to understand client contexts.
• Poor advice erodes client trust and retention.
• Better context drives more relevant advice and higher assets under management.

M&A teams evaluate deal opportunities by exploring corporate ownership structures, supply chains and partnership networks. Complex, cross-border relationships can hide risks and synergies.

• Rocketgraph analyses corporate graphs to identify indirect ownership, shared suppliers and potential antitrust concerns.
• Manual due diligence can miss hidden connections and regulatory issues.
• Unseen risks can derail deals and lead to fines.
• Graph insights speed due diligence, uncover synergies and reduce deal risk.

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Discovering new treatments and tailoring therapies to individual patients requires integrating gene expression, protein interactions, clinical trial outcomes, chemical structures and patient histories to identify hidden connections.

• Rocketgraph loads multi-modal biomedical knowledge graphs and runs graph neural networks to predict novel drug–target interactions and suggest compounds.
• Siloed data and slow manual analysis delay discovery and increase costs.
• Inefficient discovery wastes billions and delays life-saving treatments.
• Graph-driven discovery reduces time-to-market and uncovers new indications.

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Chronic and complex patients interact with multiple providers and treatments over time. Understanding their journey helps improve outcomes and reduce costs.

• Rocketgraph creates longitudinal patient graphs connecting appointments, diagnoses, labs and medications to uncover care pathways and gaps.
• Fragmented systems prevent providers from seeing the full picture.
• Poor coordination results in duplication, delays and adverse events.
• Better coordination reduces readmissions, improves satisfaction and lowers costs.

Successful trials depend on enrolling the right participants, ensuring compliance and monitoring outcomes. Matching candidates to complex criteria is time-consuming.

• Rocketgraph integrates patient registries, genomic data and trial protocols, using similarity searches to find eligible participants and track them during trials.
• Manual screening and monitoring delay recruitment and jeopardize safety.
• Delays increase costs and reduce patent windows, while missed adverse events harm participants.
• Graph-based recruitment accelerates trials and improves safety, saving millions.

During epidemics, health authorities need to track the spread of infections through populations and identify clusters and super-spreaders.

• Rocketgraph models individuals, locations and interactions as a transmission graph, enabling rapid tracing and targeted interventions.
• Traditional spreadsheets cannot capture complex contact networks.
• Inadequate tracing allows disease to spread unchecked.
• Early detection and containment save lives and reduce economic impact.

Understanding how genes regulate each other is key to identifying disease mechanisms and therapeutic targets.

• Rocketgraph builds regulatory graphs from expression data and identifies key regulators using centrality and community algorithms.
• Manual analysis cannot handle the scale and complexity of genomic interactions.
• Missing regulatory insights slows research and misdirects drug development.
• Graph analytics accelerates discovery and guides precision therapies.

Monitoring post‑market drug safety requires connecting reported adverse events to drugs, dosages and patient characteristics.

• Rocketgraph links patient reports, prescriptions and clinical data to detect unusual patterns and predict potential side effects.
• Isolated reporting systems miss weak safety signals.
• Unreported adverse events harm patients and lead to costly recalls.
• Early detection protects patients and reduces litigation and recall costs.

Healthcare networks involve hospitals, clinics, labs and specialists whose referral patterns influence efficiency and outcomes.

• Rocketgraph analyses provider-to-provider graphs to identify bottlenecks and recommend optimal referral flows.
• Inefficient networks cause delays and unnecessary costs.
• Unoptimized referral pathways lead to patient leakage and revenue loss.
• Optimized networks improve resource utilization and patient satisfaction.

Hospitals rely on complex supply chains for drugs, equipment and consumables. Disruptions cause shortages and delays in care.

• Rocketgraph maps suppliers, manufacturers, distributors and transporters to predict vulnerabilities and optimise inventory.
• Lack of visibility leads to stockouts or overstocking.
• Supply disruptions endanger patients and increase costs.
• Graph-driven planning reduces shortages, waste and costs.

Radiology, pathology and genomics provide complementary insights into disease. Correlating these modalities can improve diagnosis.

• Rocketgraph links imaging findings, biopsy results and genomic profiles to reveal multi-modal disease signatures.
• Separate systems limit integrated analysis.
• Missed correlations lead to misdiagnosis.
• Multi-modal graphs improve diagnostic accuracy and guide treatment.

Health systems need to identify at-risk populations, social determinants and intervention strategies to improve overall health.

• Rocketgraph aggregates patient data, socio-economic factors and community resources to detect high-risk cohorts and guide interventions.
• Simple statistics ignore complex interdependencies among determinants.
• Unaddressed risk factors drive up costs and worsen outcomes.
• Graph-driven insights enable targeted outreach and reduce population-level costs.

Operators run networks of towers, routers and fibre links. Optimising topology and capacity ensures reliable service and controls costs.

• Rocketgraph models devices and traffic flows as a graph, computing centrality, bottlenecks and simulating topology changes in real time.
• Legacy tools analyse segments in isolation and miss cross-domain dependencies.
• Poor optimisation leads to congestion, outages and wasted capital.
• Graph insights improve utilisation, reduce capex and enhance customer experience.

Faults propagate through interconnected network components. Quickly identifying root causes is crucial to minimise downtime.

• Rocketgraph correlates alarms, logs and topology to trace fault propagation paths and pinpoint root causes.
• Manual investigation is slow and can misdiagnose issues.
• Prolonged outages result in customer churn and SLA penalties.
• Automated graph-based diagnostics accelerate resolution and reduce downtime.

Telecom customers often switch providers due to service quality issues or better offers. Predicting churn enables targeted retention.

• Rocketgraph analyses call records, service usage and social influences within a customer graph to identify churn risk segments.
• Traditional churn models ignore network effects and peer influence.
• Unaddressed churn reduces revenue and increases acquisition costs.
• Graph-based insights enable proactive retention campaigns and increase lifetime value.

Telecom services depend on layers of infrastructure and software. Understanding dependencies is essential for impact analysis and change management.

• Rocketgraph creates service dependency graphs linking applications, servers, databases and network devices.
• Fragmented CMDBs make it hard to see end-to-end dependencies.
• Changes can inadvertently disrupt services and violate SLAs.
• Graph visualisation and impact analysis reduce downtime and change risk.

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Fraudsters exploit SIM cloning, subscription reselling and roaming arbitrage schemes.

• Rocketgraph models subscriber relationships and usage patterns to detect anomalies like shared identifiers or unusual call graphs.
• Rule-based fraud checks miss complex patterns.
• Subscription fraud drives revenue leakage.
• Graph analytics reduces fraud losses and protects network integrity.

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Efficient routing maximizes network capacity and quality-of-service. Traffic patterns change continuously.

• Rocketgraph optimizes routing paths by analyzing real-time traffic graphs and recommending re-routing.
• Static routing tables cannot adapt to dynamic conditions.
• Inefficient routing degrades performance and wastes bandwidth.
• Dynamic graph-based routing improves throughput and reduces latency.

Telecom companies can leverage call graphs to identify influential subscribers and communities for targeted promotions.

• Rocketgraph performs community detection and influence scoring on call and message networks.
• Traditional segmentation ignores social influence.
• Missed targeting opportunities reduce campaign ROI.
• Influence-based marketing improves uptake of new services and reduces churn.

Networks comprise thousands of assets with lifecycles, maintenance schedules and dependencies.

• Rocketgraph tracks assets, maintenance history and dependencies, enabling predictive maintenance and budget planning.
• Spreadsheets fail to capture complex relationships.
• Unmanaged assets cause unexpected failures and overspending.
• Graph-driven asset management extends asset life and reduces costs.

Telcos sell bundles of services (internet, mobile, TV). Understanding which combinations appeal to different customers aids revenue growth.

• Rocketgraph analyses usage patterns and relationship graphs to recommend bundles that resonate with communities.
• Guesswork bundling leads to low uptake.
• Misaligned bundles waste marketing spend and leave revenue on the table.
• Data-driven bundling increases ARPU and customer loyalty.

5G enables network slicing—dedicated virtual networks for different applications. Allocating slices requires understanding service demands and resource availability.

• Rocketgraph models service demands and resource graphs to allocate slices dynamically and monitor performance.
• Static allocation wastes resources or causes contention.
• Poor slicing leads to SLA violations for high-priority applications.
• Graph-driven allocation maximizes utilization and meets diverse service requirements.

Telcos handle millions of support tickets. Understanding the paths customers take through support channels can reveal process inefficiencies.

• Rocketgraph links interactions across IVR, chat, email and agents to map resolution journeys and identify bottlenecks.
• Siloed support systems prevent holistic analysis.
• Inefficient support increases churn and costs.
• Improving resolution paths boosts satisfaction and reduces support costs.