Financial services

Graph Database for Fraud Detection, AML and KYC

Detect fraud rings, analyse AML networks and resolve entities on a graph engine fast enough to score a payment in flight and auditable enough for a regulator.

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Financial services

Short answer: Financial crime is a network problem, and networks are queried by traversal. TuringDB runs multi-hop traversals across transaction and counterparty graphs in milliseconds (fast enough to score a payment while it is still in flight) and records every change as an immutable commit, so you can reconstruct exactly what the network looked like on the day an alert fired. That combination of in-line latency and point-in-time auditability is what most fraud and AML stacks are missing.

Why financial crime is a graph problem

Fraud does not live in a row. It lives in the relationships between rows: the device shared by nine accounts, the address that appears on both sides of a transfer, the circular flow that returns to its origin after four hops, the mule network that only becomes visible when you look two counterparties out.

Relational systems can answer one-hop questions cheaply. Each additional hop is another join, and the cost climbs exponentially. Teams respond by capping depth, pre-computing features overnight, or moving detection to batch. All three mean the pattern is found after the money has moved.

A graph database makes the hop the primitive operation instead of the expensive exception, but only if traversal is genuinely fast at depth. A graph engine that takes 18 seconds to go six hops has moved the bottleneck, not removed it.

Use cases

Real-time fraud detection

Traverse transaction chains, shared devices, shared addresses, and counterparty links while the payment is in flight. Multi-hop patterns (circular flows, layering chains, fan-out to mule accounts) surface in milliseconds rather than in tomorrow's batch job. Because reads never contend with writes in TuringDB, the scoring path stays fast while the ingest pipeline keeps writing transactions.

AML transaction network analysis

Model accounts, entities, transactions, and counterparties as one connected graph and walk it to find layering structures, smurfing patterns, and networks that no single-hop rule catches. Analysts can follow the money interactively instead of raising a ticket for an overnight job.

KYC and entity resolution

Resolve people and companies across fragmented source systems into single graph entities, then traverse ownership, directorship, and control relationships to reach ultimate beneficial owners across jurisdictions. What is a multi-day manual cross-referencing exercise becomes a query.

Sanctions screening

Screen not just the counterparty but the counterparty's network. Multi-hop traversal exposes indirect exposure through intermediaries and shell structures that a name-match check will never see.

Counterparty and concentration risk

Assess exposure across the full web of ownership, lending, and settlement relationships. Graph traversal reveals contagion paths and concentration that stay invisible in tabular views, and it is fast enough to re-run on every material market move rather than at month end.

Regulatory reporting and audit reconstruction

Native versioning records every change: what was added, what was amended, when. Reconstructing the state of an entity's network as of the day it was flagged is a query against a commit, not a restore-from-backup exercise.

What TuringDB brings to a financial crime stack

RequirementTuringDB capabilityWhy it matters here
In-line decisioning latencyMillisecond multi-hop traversal, 6-hop in 493 ms on a 3M-node benchmark graphScore before the payment settles
Ingest that never blocks analysisZero-lock concurrency on immutable DataPartsTransaction streams keep writing while detection queries run
Point-in-time reconstructionNative Git-style commits and time travelAnswer "what did this network look like on 14 March?" in one query
Defensible lineageEvery change is a commit with provenanceAudit trail built into the database, not assembled from logs
Scenario testingBranch and merge the graphTest a new rule against real data without touching production
Existing toolingCypher-compatibleAnalysts and engineers keep the query language they already use
Cost controlCompact in-memory columnar storageBillion-edge transaction graphs on ordinary hardware
SovereigntySelf-hosted, on-premise, air-gappedData stays inside the regulatory boundary

What to evaluate when choosing a graph database for financial crime

  1. Latency at your real depth, not at one hop. Benchmark the deepest pattern your analysts actually want to run, not the shallow one your current system can afford.
  2. Behaviour under concurrent write load. Detection runs while ingest runs. Ask what happens to query latency when the transaction stream is at peak.
  3. Point-in-time reconstruction. Ask specifically how the system answers a regulator's question about historical state, and how much of that answer depends on application-level logging.
  4. Total cost at graph size. Memory-priced engines get expensive precisely when the graph gets useful.
  5. Deployment boundary. On-premise and air-gapped options, and whether source access is available for review.

Frequently asked questions

What are the important criteria for a fraud detection graph database?

Two requirements dominate: millisecond multi-hop traversal under concurrent write load, and a defensible audit trail. Most graph engines optimise for transactional lookups and a large integration ecosystem, which is the right trade if the patterns you chase are shallow. Fraud rings are not shallow, and they are queried while a payment is still in flight. TuringDB focuses on depth under load instead, with columnar in-memory execution, zero-lock concurrency so ingest never blocks detection, and native versioning so a decision can be reconstructed exactly as a regulator will ask for it.

Can a graph database detect fraud in real time?

Yes, if traversal at the depth you need completes inside your decisioning window. A typical in-line window is tens of milliseconds. TuringDB returns four- to six-hop traversals on multi-million-node graphs in the hundreds of milliseconds cold and single-digit milliseconds on typical warm production patterns, which puts genuine in-line network analysis in range.

How do graph databases help with AML?

Money laundering is structured to defeat single-entity monitoring: layering spreads a transfer across intermediaries so that no individual hop looks suspicious. Graph traversal follows the whole chain, which is the only view in which the structure is visible. Adding versioning lets an investigator see the network as it stood at the time of the activity rather than as it stands today.

Does TuringDB support point-in-time queries for regulators?

Yes. Every change is an immutable commit, and any historical commit can be queried at full speed. There is no restore step and no dependency on log replay.

Can TuringDB run inside our regulatory perimeter?

Yes. TuringDB is self-hostable with full source access, and runs on-premise or air-gapped.

A plain-markdown version of this page is available at use-cases/finance.md.