AI for SIU Investigators

What you do today

You connect dots across multiple claims to identify coordinated fraud — shared addresses, rotating attorneys, staged accident patterns, and recruited claimants.

AI that applies

Network analysis AI maps relationships across thousands of claims, identifying clusters of connected individuals, addresses, and providers that indicate organized activity.

How it works

For identify organized fraud rings, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

You stake out claimants, photograph activity inconsistent with claimed injuries, and document findings for potential denial or prosecution referral.

AI that applies

AI-assisted video analysis can timestamp and tag activity in surveillance footage, and geolocation tools help plan optimal surveillance positions.

How it works

For conduct field surveillance, the system draws on the relevant operational data and applies the appropriate analytical models. Computer vision models analyze the visual input by detecting objects, measuring spatial relationships, and comparing against trained reference patterns to identify matches or anomalies. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Each morning you open a queue of claims flagged by adjusters or tip lines, reading through loss details and deciding which ones warrant a full investigation.

AI that applies

AI scoring models rank incoming referrals by fraud probability, highlighting red flags like prior claim history, provider patterns, and timeline inconsistencies before you even open the file.

How it works

For review new referrals for investigation, the system draws on the relevant operational data and applies the appropriate analytical models. Predictive models fit to historical outcome data identify which variables are the strongest leading indicators, then apply those weights to current inputs to generate forward-looking scores. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

You search NICB, ISO ClaimSearch, public records, social media, and internal databases to build a profile on subjects and identify prior claim activity.

AI that applies

AI aggregates results across multiple databases simultaneously, surfaces hidden connections between claimants, providers, and attorneys, and maps social networks.

How it works

The system ingests databases simultaneously as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — hidden connections between claimants — surfaces in the existing workflow where the practitioner can review and act on it.

What you do today

You review billing records from medical providers to identify upcoding, unbundling, phantom billing, or treatment patterns that don't match injury severity.

AI that applies

AI compares billing patterns across thousands of providers, flagging statistical outliers in procedure frequency, billing amounts, and patient overlap.

How it works

The system ingests clinical data — patient records, lab results, vitals, and care history from the EHR. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

You compile findings into structured reports for claims management, legal counsel, or law enforcement referral — organizing evidence, timelines, and witness statements.

AI that applies

AI drafts report templates from your case notes and evidence log, organizing findings chronologically and flagging gaps in documentation.

How it works

The system ingests case notes and evidence log as its primary data source. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The output is a structured view that highlights exceptions, trends, and items requiring attention — available in the existing tools without switching systems.

What you do today

You interview claimants, witnesses, and providers — asking probing questions, reading body language, and documenting inconsistencies in their narratives.

AI that applies

AI transcription captures statements in real time, and sentiment analysis can flag vocal stress patterns or narrative inconsistencies during or after interviews.

How it works

For conduct recorded statements, the system draws on the relevant operational data and applies the appropriate analytical models. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

When cases warrant criminal referral, you package evidence for district attorneys or federal investigators and serve as a liaison throughout prosecution.

AI that applies

AI helps compile evidence packages in formats prosecutors prefer, cross-referencing related cases and identifying additional defendants for ring investigations.

How it works

For coordinate with law enforcement and prosecutors, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

You present findings in depositions, arbitrations, and trials — defending your investigation methodology and conclusions under cross-examination.

AI that applies

AI helps you prepare by organizing case chronology, anticipating defense arguments based on similar cases, and generating exhibit timelines.

How it works

The system reads contract text and legal documents, extracting clauses, obligations, and risk indicators. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

You track emerging fraud schemes — telemedicine abuse, rideshare staging, cryptocurrency laundering — and update your team's detection playbooks accordingly.

AI that applies

AI monitors claims data for emerging patterns and can surface new scheme types before they become widespread, learning from confirmed fraud outcomes.

How it works

The system ingests claims data for emerging patterns and can surface new scheme types before they b as its primary data source. Machine learning establishes a baseline of normal patterns from historical data, then flags any new observation that deviates beyond the learned thresholds. The output — new scheme types before they become widespread — surfaces in the existing workflow where the practitioner can review and act on it.