Financial crime that hides in the relationships.
Rules engines fire on transactions. Money launderers move through relationships: 4-hop sanctions chains, synthetic identity rings, coordinated trading across business lines, beneficial ownership buried in offshore structures. Purple8 models the entire entity network as a graph. The pattern a rule can never see is a single traversal query.
4-hop
Sanctions chain traced instantly
95%
False positives eliminated
14โ1
Enterprise services replaced
The problem
Rules look at transactions. Crime moves through relationships.
Every financial institution runs a rules engine. Rules fire on individual transactions: amount thresholds, velocity, jurisdiction flags. Financial criminals have understood this for thirty years. They structure below thresholds, route through multiple hops, use nominee entities, and operate across business lines precisely because rules cannot follow them there.
The 95% false positive rate that plagues every AML programme is not a tuning problem. It is a structural one. Rules were never designed to see relationships, and relationships are where the crime is.
Purple8 is not a better rules engine. Every account, customer, transaction, beneficial owner, device, and watchlist entry is a node in the graph. Financial crime detection is a traversal query.
CUSTOMER โ ACCOUNTHigh-risk retail client
ACCOUNT โ SHELL_COTransfer to BVI entity
SHELL_CO โ OFFSHORE_ACCTWire to Cayman LP
OFFSHORE_ACCT โ SDN_ENTITY2-hop from OFAC sanctioned person
SDN_ENTITY โ VOLKOV_NNikolai Volkov - OFAC SDN list
A rules engine sees transaction 1. Purple8 traverses all 5 hops and traces the full sanctions chain in a single query.
Solution patterns
Ten financial crime typologies on one graph engine.
Each pattern below is a graph query, not a separately configured rule or a new product integration.
Multi-Hop Money Laundering
AML ยท Transaction Monitoring
The placement, layering, and integration chain is invisible to any rule that examines one transaction at a time. Purple8 traverses the full entity network at 4, 6, or 8 hops and traces the path from source funds to OFAC SDN in milliseconds. A chain that would take an investigator two weeks to reconstruct manually is a single graph query.
Transaction Screening
Sanctions ยท Watchlist
Two-tier SDN screening runs an offline local pattern match first, then an optional real-time API call to ofac.io, Dow Jones Watchlist, Refinitiv World-Check, or ComplyAdvantage. Every screened entity is a graph node with full relationship context. A name that clears direct screening but sits two hops from a sanctioned entity is still flagged, because the relationship graph catches what name-matching misses.
Fraud Ring Detection
Fraud ยท Identity
Fraud rings share devices, addresses, phone numbers, and IP addresses across seemingly unrelated accounts. Community detection clusters accounts that share any attribute node. PageRank identifies the most influential accounts in the network. Betweenness centrality finds the key intermediaries. A synthetic identity ring of 40 accounts spanning 3 years surfaces as a single connected component.
Beneficial Ownership & KYC
KYC ยท EDD ยท Corporate Structures
Multi-hop traversal maps the full ownership chain through nominee directors, Cayman LPs, BVI shells, and Singapore SPVs to the ultimate beneficial owner. DocIntel ingests company registry documents, KYC forms, and adverse media references and emits entities directly to the graph. A 5-entity offshore structure resolves to its UBO in a single traversal call.
Elder Financial Abuse
Vulnerable Customer Protection
A new person appears, takes Power of Attorney, and starts moving money. Purple8's Change Data Capture fires the moment a new CONTROLS or POA_FOR edge is written. Link prediction scores new beneficiaries with no prior relationship as anomalous. Community detection surfaces one person controlling multiple elderly customers across the book, a pattern that no per-account rule can see.
Structuring & Smurfing
BSA ยท CTR Avoidance
Multiple sub-$10,000 cash deposits by the same individual, related accounts, or shared devices constitute structuring, a federal crime. Purple8 aggregates across accounts linked by shared device, address, or relationship edge. The aggregated pattern that evades the CTR threshold surfaces as a typology match against the structuring pattern graph.
Trade-Based Money Laundering
TBML ยท Cross-Border
Over- and under-invoicing between related trading entities is the largest money laundering typology by volume and the hardest to detect. Purple8 connects the trade finance graph, covering invoices, letters of credit, counterparties, and jurisdictions, to the corporate ownership graph. When buyer and seller share a beneficial owner, the relationship edge makes the manipulation visible.
Market Surveillance
Prime Brokerage ยท Securities
Spoofing, layering, bear raids, wash trading, and dividend arbitrage leave traces in the order graph. Purple8 models positions, orders, cancellations, and beneficial ownership as a unified graph. Two hedge funds shorting the same illiquid mid-cap on the same days via a shared prime account is a community detection result, not a manually configured alert rule.
SAR Filing & Case Management
Workflow ยท Regulatory Reporting
The SAR filing workflow runs as a Journey Engine state machine: alert, triage, investigation, supervisor review, FIU escalation, narrative generation, filing. Each stage transition is a mandatory human decision recorded as an immutable graph edge. The SAR narrative is generated from the actual graph evidence, including the relationship chain, transaction timeline, and typology match, not reconstructed from memory after the fact.
Cross-Business-Line Intelligence
Group Financial Crime ยท Enterprise
The same individual appearing in Retail Banking, Asset Management, and Prime Brokerage with different risk profiles is the most dangerous customer in the book. Purple8's multi-tenant architecture keeps business line data isolated for regulatory purposes while letting Group Financial Crime query across all tenants. Cross-divisional alerts are graph edges, not email threads.
Side by side
Rules engine vs. graph intelligence
| Capability | Rules Engine / Legacy AML | Purple8 โฆ |
|---|---|---|
| Relationship depth | 1-hop (direct transaction parties) | Unlimited hops across the full network |
| New pattern detection | Requires a new rule to be written | Graph algorithms surface emergent patterns |
| False positive rate | 95โ98% (industry average) | Relationship context filters out noise |
| Investigation time | Days to weeks per case | Full chain traversal on demand |
| Cross-business-line | Manual coordination and email threads | Single cross-tenant graph query |
| SAR narrative | Manual investigator write-up | Generated from graph evidence |
| Audit trail | Reconstructed from logs | Immutable, every edge timestamped at write time |
Who uses this
Built for every institution that has a financial crime problem
Tier 1 & Tier 2 Banks
Replace the financial crime stack, including transaction monitoring, case management, sanctions screening, and SAR workflow, or deploy Purple8 as the investigation and intelligence layer on top of existing systems. The entry point is wherever investigator time is most wasted.
Asset Managers & Prime Brokers
Market surveillance, beneficial ownership resolution, and cross-business-line intelligence in a single platform. The same graph that traces a sanctions chain in retail banking surfaces coordinated short-selling in the prime brokerage book.
Payments & Fintech
Fraud ring detection and real-time transaction screening at API speed. The graph engine runs in-process with no external database and no network hop, giving sub-millisecond entity resolution at transaction throughput.
Regulators & FIUs
Financial Intelligence Units receive SARs from hundreds of institutions. Purple8 can model the national transaction network as a graph for systemic financial crime intelligence that no single institution's data can provide.
Compliance Technology Vendors
Embed Purple8 as the graph intelligence engine inside your existing compliance platform. Replace the relationship layer of your rules engine with a graph traversal API. Your clients get relationship-aware alerts and you get a technical moat that takes years to replicate.
FAQ
Questions from compliance and technology teams
How is this different from a rules-based AML system?
Rules engines fire alerts on individual transactions. Purple8 models the entire entity network as a graph: accounts, customers, transactions, beneficial owners, devices, and watchlist entries are all nodes and edges. A 4-hop OFAC sanctions chain is invisible to any rule that looks at one transaction at a time. Graph traversal finds it in milliseconds.
Does Purple8 replace existing AML transaction monitoring systems?
It can replace the entire stack, including transaction monitoring, case management, sanctions screening, SAR filing workflow, and audit trail, or sit alongside existing systems as the investigation layer. Most institutions start with investigation because that is where the most investigator time is lost.
How does transaction screening work?
Purple8 has native OFAC and sanctions screening built into the core. It runs two-tier SDN screening: an offline local pattern match that is instant and requires no API, followed by an optional real-time call to ofac.io or a commercial provider such as Dow Jones, Refinitiv, or ComplyAdvantage. Every screened entity is a node in the graph with full relationship context, not an isolated alert.
How does Purple8 detect elder financial abuse?
Elder financial abuse follows a consistent graph pattern. A new person appears, takes Power of Attorney, and starts moving money. Purple8's Change Data Capture fires the moment a new CONTROLS or POA_FOR edge is written to the graph. Link prediction scores the new beneficiary as anomalous if they have no prior relationship with the account holder. Community detection surfaces one person controlling multiple elderly customers across the book, something no per-account rule can see.
Can Purple8 handle multiple business lines in one graph?
Yes. Purple8's multi-tenant architecture partitions data by tenant, so Retail Banking, Asset Management, and Prime Brokerage each have isolated namespaces, while Group Financial Crime can query across all of them. The same individual appearing in Retail Banking and Prime Brokerage is surfaced automatically. This is the cross-divisional intelligence that financial institutions have been trying to achieve for a decade.
Capabilities used in this workflow
GraphRAG โ
AI explanations citing real graph evidence
Workflow Orchestration โ
SAR filing state machine with HITL gates
Agentic Process Automation โ
Alert triage to FIU escalation, automated
Scenario Simulation โ
Network stress-testing and freeze simulation
Quantum Optimisation โ
Optimal asset freeze under investigation constraints
Multi-Agent Systems โ
Parallel investigation across business lines
See a 4-hop sanctions chain traced live
The demo runs on real IBM AML data across three business lines. It takes 20 minutes and shows every pattern on this page in action.