Signals are fragmented
Claims, invoices, location data, attestations, provider records, participant activity, and other evidence sit in separate systems.
Service-as-Software for Fraud Detection & Investigation
Detect Fraud Earlier and Build Investigation-Ready Evidence
Labrynth detects suspicious claims and transactions, connects patterns across entities and networks, and turns high-risk activity into traceable, investigation-ready evidence. AI fraud detection software for complex claims, payments, public programs, and fraud investigation workflows.
Transaction-level signals · Entity risk scoring · Network analysis · Investigation evidence · Human review
The problem
The highest-risk patterns emerge across transactions, entities, locations, and time
Traditional review often checks one claim or payment at a time. Coordinated fraud can remain invisible when the signal only appears after multiple data streams, people, providers, locations, invoices, and historical behaviors are connected.
Rules miss relationships
A transaction may look valid in isolation while becoming suspicious when viewed against connected entities or repeated behavior.
Investigators face noise
Large alert volumes force review teams to decide which cases deserve attention before enough evidence has been assembled.
Evidence is rebuilt manually
Once a case is opened, analysts often spend significant time reconstructing the timeline, source records, links, and decision trail.
- fraud detection software
- fraud analytics software
- fraud ring detection
What it is
Fraud detection should connect the signal to the investigation
Modern fraud detection software uses rules, analytics, behavioral signals, and relationship data to identify suspicious activity. Labrynth extends detection into investigation by connecting high-risk transactions to the entities, networks, evidence, and review actions needed to understand what happened and decide what to do next.
- fraud detection software
- fraud investigation software
- fraud management software
- fraud analytics software
- payment integrity software
- fraud case management software
Detection architecture
Move from isolated anomalies to entity and network-level fraud signals
Detection works across three connected review layers, so activity that looks acceptable on its own can still be surfaced once the wider pattern is considered.
01
Transaction and data verification
Validate individual claims, invoices, timestamps, locations, durations, attestations, and other configured data points for anomalies and inconsistencies.
02
Entity risk analysis
Combine signals across providers, workers, participants, vendors, recipients, or other entities to identify repeated suspicious behavior and elevate high-risk cases.
03
Network and relationship analysis
Connect shared addresses, transactions, actors, locations, behaviors, and relationships to expose coordinated fraud rings or collusive patterns that single-record checks can miss.
- graph fraud detection
- link analysis fraud detection
- fraud ring detection
- graph analytics fraud detection
How it works
From detection to fraud investigation
- 1
Ingest and validate source data
Connect the claims, payment, program, provider, participant, location, or supporting datasets used in the fraud review scope.
- 2
Generate fraud signals
Apply configured checks across anomalies, duplicates, timing, geography, behavior, data quality, and other relevant indicators.
- 3
Score and prioritize risk
Combine available evidence to prioritize suspicious transactions, entities, and networks for investigation rather than treating every alert equally.
- 4
Open an investigation
Bring linked records, related entities, source evidence, and relevant risk signals into one investigator-ready case view.
- 5
Build the evidence trail
Preserve source records, relationships, analyst actions, review notes, and decisions so the investigation can be audited and defended.
- 6
Support the next action
Route the case for review, approval, rejection, escalation, remediation, recovery, or other configured downstream action while preserving the digital footprint.
Signals and evidence
Signals Labrynth can bring into the review
These are examples of configurable signals drawn from Labrynth's public-program work. The signals available in any deployment depend on the data and requirements configured for that program.
Location and velocity
Compare location and timing evidence to identify impossible or implausible service patterns that warrant investigation.
Timestamp and duration
Check whether service times, durations, sequences, and related records are internally consistent.
Duplicate billing
Identify repeated invoices, duplicate claims, repeated service records, or suspicious payment duplication.
Entity relationships
Surface shared addresses, providers, workers, participants, vendors, accounts, or other relationships across suspicious activity.
Spend and behavior patterns
Flag unusual spending velocity, disproportionate utilization, repeated maximum claims, or behavior that deviates from expected patterns.
Attestation and evidence quality
Use available attestations, signed records, supporting evidence, and data-quality signals to strengthen or weaken confidence in a case.
Use cases
Identify fraud and improper payments across complex, high-volume programs
The common product is the fraud-detection and investigation workflow, while each program configures its own data, requirements, risk signals, and review actions.
Public benefits and social programs
Detect suspicious claims, provider activity, participant patterns, improper payments, and coordinated behavior across publicly funded programs.
Healthcare payment integrity and FWA
Support fraud, waste, and abuse analysis by connecting claims, providers, billing patterns, and investigation evidence. This workflow is separate from Labrynth's healthcare regulatory-document compliance offering.
- payment integrity software
- FWA analytics
Provider and claims fraud
Identify duplicate billing, unusual utilization, impossible service patterns, suspicious provider behavior, and related claim anomalies.
Improper payments
Prioritize payments and claims that require additional review before or after funds move while preserving the evidence needed for investigation.
Network and collusive fraud
Expose coordinated behavior across multiple people, providers, accounts, addresses, vendors, or organizations using link and graph analysis.
- graph fraud detection
- fraud ring detection
Outcomes
Prioritize the fraud that matters and preserve the evidence needed to investigate it
Labrynth is designed to surface high-risk activity earlier, reduce manual evidence gathering, and give investigators a traceable path from the original signal to the final case decision.
Earlier fraud detection
Surface suspicious behavior at the transaction, entity, and network level before it disappears inside large volumes of normal activity.
Better case prioritization
Focus investigator attention on higher-risk claims, entities, and networks instead of reviewing alerts in isolation.
Faster evidence assembly
Bring linked source data, relationships, signals, and case history together instead of reconstructing the investigation manually.
Defensible audit trail
Preserve the data, analyst actions, decisions, and investigation evidence behind each reviewed case.
Proof point
Fraud and improper-payment detection for Australia's NDIS
Labrynth is deploying AI-driven fraud and improper-payment detection at national scale for Australia's $15 billion NDIS program. The system is engineered to surface suspicious activity at the transaction level, in near real time, before funds move.
Transaction-level analysis
Detect anomalies and suspicious activity inside high-volume program data rather than relying only on post-event manual audits.
Entity and network context
Connect providers, participants, workers, claims, and related records to reveal patterns that are not visible in a single transaction.
Investigation-ready evidence
Preserve the digital footprint and assemble the information investigators need to review suspicious activity and determine the appropriate action.
Who it is for
Built for the teams that detect, investigate, and act on fraud
Program integrity leaders
Public-sector leaders responsible for fraud, waste, abuse, improper payments, and integrity across large benefit or service programs.
Fraud investigators
Analysts who need prioritized cases, connected evidence, network context, and a traceable investigation record.
Payment integrity teams
Teams reviewing claims and payments for anomalies, FWA, provider risk, and improper-payment exposure.
Risk and compliance leaders
Organizations that need repeatable fraud controls, review history, evidence preservation, and defensible remediation workflows.
Why Labrynth
Connect detection, network analysis, investigation, and evidence in one review workflow
Adjacent fraud tools
Labrynth
Rules-only checks that flag one transaction at a time
Transaction signals combined with entity and network-level context
Generic anomaly alerts with limited investigation context
Prioritized cases with linked source evidence and relationship analysis
Fraud-prevention tools designed mainly to block card or ecommerce transactions
Configurable detection and investigation for claims, payments, public programs, and complex fraud workflows
Separate case tools that require analysts to rebuild evidence manually
Traceable investigation records that preserve signals, source data, relationships, actions, and case decisions
Existing systems
Connect the data that already contains the fraud signal
Labrynth works as an intelligence and investigation layer across existing claims, payment, program, provider, document, and case environments rather than requiring every operational system to be replaced.
Claims and transaction data
Invoices, claims, payments, service records, timestamps, locations, attestations, and other operational data relevant to the fraud model.
Entity and program data
Provider, participant, worker, vendor, account, address, organization, eligibility, and relationship data needed to build context.
Investigation systems
Existing case management, analyst review, reporting, and downstream remediation systems can remain part of the operating workflow.
Book a demo
See how Labrynth finds high-risk activity in your program data
Show us the program, claims or transaction environment, known fraud patterns, investigation process, and the data available today. We will demonstrate how Labrynth can detect signals, connect related entities, prioritize cases, and build investigation-ready evidence.
FAQ
Frequently asked questions
What is fraud detection software?
Fraud detection software analyzes transactions, claims, entities, behaviors, and related data to identify activity that warrants additional review. Labrynth combines transaction-level signals with entity and network analysis and then carries high-risk cases into a traceable investigation workflow.
How does Labrynth detect fraud?
Labrynth connects configured data sources, validates individual records for anomalies, combines signals across related entities, analyzes suspicious networks, and prioritizes cases based on the evidence available for the specific program.
How is fraud investigation different from fraud detection?
Detection identifies suspicious activity. Investigation determines why the activity is suspicious by bringing together the source data, connected entities, relationships, evidence, analyst actions, and decision history needed to review the case.
Does Labrynth automatically block suspicious payments?
Not necessarily. The workflow can flag suspicious activity for review instead of automatically blocking it, allowing investigators to preserve the digital footprint and make the appropriate decision based on the program's policy and evidence.
Can Labrynth detect fraud rings and coordinated activity?
Yes, where the necessary relationship data is available. Labrynth can connect providers, participants, workers, vendors, accounts, locations, claims, or other entities to surface suspicious networks and patterns that may not be visible in an individual transaction.
Can Labrynth support payment integrity and fraud, waste, and abuse workflows?
Yes. Claims and payment environments can use Labrynth to identify suspicious billing, improper-payment patterns, provider risk, duplicate activity, and related evidence for investigation. This fraud workflow is separate from Labrynth's healthcare regulatory-document compliance offering.
Is Labrynth fraud prevention software?
Labrynth can support fraud-prevention objectives by identifying suspicious activity earlier, but the core positioning is fraud detection and investigation. It does not guarantee that all fraudulent transactions will be blocked before they occur.
Is Labrynth document fraud detection software?
No. Labrynth is not primarily designed to detect forged IDs or manipulated documents. Its anti-fraud workflow analyzes claims, transactions, entity relationships, behavioral signals, and investigation evidence.
Does Labrynth replace fraud investigators?
No. Labrynth prioritizes suspicious activity, connects evidence, and organizes the investigation trail. Human investigators and authorized decision-makers retain judgment, escalation, remediation, and enforcement responsibility.
What kinds of organizations can use Labrynth for fraud detection?
The same architecture can support public benefits, healthcare payment integrity, provider and claims fraud, improper-payment programs, and other high-volume environments where suspicious activity must be detected and investigated across connected data.
Detect high-risk activity earlier.
Book a demonstration built around your program data, the fraud patterns you already know about, and the investigation workflow your team runs today.
Request a demo